Noteworthy advancements in the field of deep learning have led to the rise of highly realistic AI generated fake videos, these videos are commonly known as Deepfakes. They refer to manipulated videos, that are generated by sophisticated AI, that yield formed videos and tones that seem to be original. Although this technology has numerous beneficial applications, there are also significant concerns about the disadvantages of the same. So there is a need to develop a system that would detect and mitigate the negative impact of these AI generated videos on society. The videos that get transferred through social media are of low quality, so the detection of such videos becomes difficult. Many researchers in the past have done analysis on Deepfake detection which were based on Machine Learning, Support Vector Machine and Deep Learning based techniques such as Convolution Neural Network with or without LSTM .This paper analyses various techniques that are used by several researchers to detect Deepfake videos.
DeepFake Detection: Challenges, Progress and Hands-on Demonstration of Techno...Symeon Papadopoulos
Slides accompanying an online webinar on DeepFake Detection and a hands-on demonstration of the MeVer DeepFake Detection service. The webinar is supported by the US-Paris Tech Challenge award for our work on the InVID-WeVerify plugin.
Deepfake detection models require clean training data to generalize well. The document discusses preprocessing training data by filtering out false detections from face extraction. This improved log loss error on evaluation datasets for models trained with the preprocessed data. However, deepfake detection remains challenging due to limited generalization, overfitting, and the broad scope of possible manipulations. The importance of preprocessing training data and methods to address challenges are discussed.
Deepfakes - How they work and what it means for the futureJarrod Overson
Deepfakes originally started as cheap costing but believable video effects and have expanded into AI-generated content of every format. This session dove into the state of deepfakes and how the technology highlights an exciting but dangerous future.
This document discusses deepfakes, which are synthetic media that uses artificial intelligence to replace a person's face or body with someone else's. It describes the origin of deepfakes from a Reddit community in 2017 and how applications now allow users to easily create and share manipulated videos. The document explains that deepfakes work using autoencoders and generative adversarial networks to learn from data and generate new realistic images and videos. It also covers methods for detecting deepfakes and discusses both the potential positive and negative applications of this emerging technology.
Deepfakes: An Emerging Internet Threat and their DetectionSymeon Papadopoulos
Webinar talk in the context of the AI4EU Web Cafe. Recording of the talk available on: https://youtu.be/wY1rvseH1C8
Deepfakes have emerged for some time now as one of the largest Internet threats, and even though their primary use so far has been the creation of pornographic content, the risk of them being abused for disinformation purposes is growing by the day. Deepfake creation approaches and tools are continuously improving in terms of result quality and ease of use by non-experts, and accordingly the amount of deepfake content on the Internet is quickly growing. For that reason, approaches for deepfake detection are a valuable tool for media companies, social media platforms and ultimately citizens to help them tell authentic from deepfake generated content. In this presentation, I will be presenting a short overview of the developments in the field of deepfake detection, and present our lessons learned from working on the problem in the context of the Deepfake Detection Challenge and from developing a service for the H2020 WeVerify project.
Deepfake technology uses artificial intelligence to manipulate or generate visual and audio content where individuals can be inserted into videos and images. This document discusses the origin and development of deepfakes, including early uses on Reddit and later mobile apps. It outlines advantages like training videos but also significant disadvantages such as creating fake identities for politics or pornography without consent. The document provides tips on spotting deepfakes by looking for unnatural facial expressions, movements, or image qualities that seem manipulated.
This document discusses deepfakes, including what they are, their history, present uses, future challenges, and consequences. Deepfakes use deep learning techniques like GANs to manipulate images and audio to deceive viewers into thinking something is real when it is actually fake. While initially developed by researchers, open-source tools now allow anyone to generate deepfakes. The future poses challenges around reducing training data needs, improving temporal coherence in videos, and preventing identity leakage, among other issues. Deepfakes could potentially target politicians, actors and public figures to manipulate perceptions. Prevention strategies include developing counter-AI techniques, using blockchain, and raising awareness.
Deepfakes use deep learning techniques to manipulate faces in images and videos, commonly swapping one person's face for another's. This technique has become widespread due to large public databases, advances in deep learning that automate editing, and apps that allow amateurs to create fakes. While detection methods have improved, fully foolproof detection remains elusive as fakes evolve. The document outlines four main facial manipulation techniques - entire face synthesis, identity swap, attribute manipulation, and expression swap - and discusses challenges in detecting fakes under each. It concludes that more research is still needed, particularly to detect fakes that have been modified to evade existing detection methods.
DeepFake Detection: Challenges, Progress and Hands-on Demonstration of Techno...Symeon Papadopoulos
Slides accompanying an online webinar on DeepFake Detection and a hands-on demonstration of the MeVer DeepFake Detection service. The webinar is supported by the US-Paris Tech Challenge award for our work on the InVID-WeVerify plugin.
Deepfake detection models require clean training data to generalize well. The document discusses preprocessing training data by filtering out false detections from face extraction. This improved log loss error on evaluation datasets for models trained with the preprocessed data. However, deepfake detection remains challenging due to limited generalization, overfitting, and the broad scope of possible manipulations. The importance of preprocessing training data and methods to address challenges are discussed.
Deepfakes - How they work and what it means for the futureJarrod Overson
Deepfakes originally started as cheap costing but believable video effects and have expanded into AI-generated content of every format. This session dove into the state of deepfakes and how the technology highlights an exciting but dangerous future.
This document discusses deepfakes, which are synthetic media that uses artificial intelligence to replace a person's face or body with someone else's. It describes the origin of deepfakes from a Reddit community in 2017 and how applications now allow users to easily create and share manipulated videos. The document explains that deepfakes work using autoencoders and generative adversarial networks to learn from data and generate new realistic images and videos. It also covers methods for detecting deepfakes and discusses both the potential positive and negative applications of this emerging technology.
Deepfakes: An Emerging Internet Threat and their DetectionSymeon Papadopoulos
Webinar talk in the context of the AI4EU Web Cafe. Recording of the talk available on: https://youtu.be/wY1rvseH1C8
Deepfakes have emerged for some time now as one of the largest Internet threats, and even though their primary use so far has been the creation of pornographic content, the risk of them being abused for disinformation purposes is growing by the day. Deepfake creation approaches and tools are continuously improving in terms of result quality and ease of use by non-experts, and accordingly the amount of deepfake content on the Internet is quickly growing. For that reason, approaches for deepfake detection are a valuable tool for media companies, social media platforms and ultimately citizens to help them tell authentic from deepfake generated content. In this presentation, I will be presenting a short overview of the developments in the field of deepfake detection, and present our lessons learned from working on the problem in the context of the Deepfake Detection Challenge and from developing a service for the H2020 WeVerify project.
Deepfake technology uses artificial intelligence to manipulate or generate visual and audio content where individuals can be inserted into videos and images. This document discusses the origin and development of deepfakes, including early uses on Reddit and later mobile apps. It outlines advantages like training videos but also significant disadvantages such as creating fake identities for politics or pornography without consent. The document provides tips on spotting deepfakes by looking for unnatural facial expressions, movements, or image qualities that seem manipulated.
This document discusses deepfakes, including what they are, their history, present uses, future challenges, and consequences. Deepfakes use deep learning techniques like GANs to manipulate images and audio to deceive viewers into thinking something is real when it is actually fake. While initially developed by researchers, open-source tools now allow anyone to generate deepfakes. The future poses challenges around reducing training data needs, improving temporal coherence in videos, and preventing identity leakage, among other issues. Deepfakes could potentially target politicians, actors and public figures to manipulate perceptions. Prevention strategies include developing counter-AI techniques, using blockchain, and raising awareness.
Deepfakes use deep learning techniques to manipulate faces in images and videos, commonly swapping one person's face for another's. This technique has become widespread due to large public databases, advances in deep learning that automate editing, and apps that allow amateurs to create fakes. While detection methods have improved, fully foolproof detection remains elusive as fakes evolve. The document outlines four main facial manipulation techniques - entire face synthesis, identity swap, attribute manipulation, and expression swap - and discusses challenges in detecting fakes under each. It concludes that more research is still needed, particularly to detect fakes that have been modified to evade existing detection methods.
This is a presentation for Brandeis International Business School's Big Data II course about newer technologies using artificial intelligence, mainly the recently trendy Deepfake.
Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by latest technological advances in AI and machine learning, they offer automated procedures to create fake content that is harder and harder to detect to human observers. The possibilities to deceive are endless, including manipulated pictures, videos and audio, that will have large societal impact. Because of this, organizations need to understand the inner workings of the underlying techniques, as well as their strengths and limitations. This article provides a working definition of deepfakes together with an overview of the underlying technology. We classify different deepfake types: photo (face- and body-swapping), audio (voice-swapping, text to speech), video (face-swapping, face-morphing, full body puppetry) and audio & video (lip-synching), and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, Expose deepfakes early, Advocate for legal protection and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter the deepfake tricks as we appreciate its treats.
Deepfakes are synthetic media that uses artificial intelligence to realistically manipulate images and videos by replacing a person's face with another. The term is a combination of "deep learning" and "fake". While deepfake technology was initially developed for entertainment purposes like special effects, it can also be used to impersonate people, create realistic simulations for training, and generate fake content for social media. However, there are disadvantages like using deepfakes for blackmail, spreading misinformation, and lack of authenticity which is why regulation of this technology is important.
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)SSII
This document provides an overview of deepfake generation and detection. It begins with an introduction to the author and their background and research interests. The rest of the document is outlined as follows: definitions of deepfakes, various deepfake generation techniques including face synthesis, manipulation, reenactment and swapping, and an overview of deepfake detection methods including commonly used datasets, image-based and video-based detection approaches.
The “deepfake” phenomenon — using machine learning to generate synthetic video, audio and text content — is an ominous example of how quickly new technologies can be diverted from their original purposes. Month by month, it is becoming easier and cheaper to create fakes that are increasingly difficult to distinguish from genuine artefacts.
This document describes various algorithms used to build a facial emotion recognition system, including Haar cascade, HOG, Eigenfaces, and Fisherfaces. It explains how each algorithm works, such as how Haar cascade detects facial features and HOG extracts histograms of gradients. The system is trained on the CK+ dataset and uses Eigenface and Fisherface classifiers to classify emotions, achieving higher accuracy (86.54%) with Fisherfaces. It provides code snippets of key steps like cropping, resizing images, splitting data, and predicting emotions.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
This document describes a deep learning algorithm to classify videos as deepfakes or authentic. It discusses deepfakes, how they are created, the system architecture including data preprocessing, a ResNext-50 model architecture with LSTM and training workflow. Results show models trained on different datasets and frame sequences achieving accuracies from 84% to 98%. The project uses PyTorch and Django with Google Cloud Platform for computing.
This document describes a project to build a convolutional neural network (CNN) model to recognize six basic human emotions (angry, fear, happy, sad, surprise, neutral) from facial expressions. The CNN architecture includes convolutional, max pooling and fully connected layers. Models are trained on two datasets - FERC and RaFD. Experimental results show that Model C achieves the best testing accuracy of 71.15% on FERC and 63.34% on RaFD. Visualizations of activation maps and a prediction matrix are provided to analyze the model's performance and confusions between emotions. A live demo application is also developed using OpenCV to demonstrate real-time emotion recognition from video frames.
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
This document discusses face recognition systems and the use of artificial neural networks for face recognition. It describes the basic steps in a face recognition system as face detection, alignment, feature extraction, and matching. Two types of neural networks that can be used for recognition are described - Radial Basis Function Networks and Back Propagation Networks. RBF Networks have an input, hidden, and output layer while BPN uses backpropagation of errors to adjust weights. The document also outlines some applications of face recognition systems such as ID verification and criminal investigations.
This document provides an overview of face recognition technology. It discusses 2D and 3D facial recognition, how the technology works by measuring facial features to create a unique face print, hardware and software requirements, advantages like identifying repeat offenders, and applications in security, multimedia, and law enforcement. The conclusion states that while progress has been made, continued work is needed to develop more accurate systems.
Computer vision is a field of artificial intelligence that uses digital images and deep learning to teach machines to interpret and understand visual input. Early experiments in computer vision in the 1950s used neural networks to detect edges and classify simple shapes, while the 1970s saw the first commercial application in optical character recognition. Today, computer vision can perform tasks like facial recognition, object detection in images and video, and image segmentation, classification, and analysis that rival and exceed human visual abilities. Computer vision works by acquiring an image, processing it through machine learning models, and understanding what is depicted to take appropriate actions.
This document provides an overview of Kalyan Acharjya's proposed work on face recognition for his M.Tech dissertation. It discusses conducting literature research on existing face recognition techniques, identifying challenges in real-time applications, and exploring standard face image databases. The presentation covers topics such as how face recognition works, applications, and concludes with plans to modify existing algorithms and compare results to related work to enhance recognition rates.
Face detection and recognition using surveillance camera2 editedSantu Chall
The document discusses face detection and recognition technology using surveillance cameras. It describes how face recognition systems work by detecting 80 nodal points on the face and creating a "face print" code based on distance measurements. The document outlines general face recognition steps including face detection, normalization, and identification. It discusses advantages like convenience and passive identification, and disadvantages like inability to distinguish identical twins. Potential applications described include security, law enforcement, immigration, and banking. The document proposes a 12-week project plan to develop a face recognition system prototype.
This document presents information on face detection techniques. It discusses image segmentation as a preprocessing step for face detection. Some common segmentation methods are thresholding, edge-based segmentation, and region-based segmentation. Face detection can be classified as implicit/pattern-based or explicit/knowledge-based. Implicit methods use techniques like templates, PCA, LDA, and neural networks, while explicit methods exploit cues like color, motion, and facial features. One method discussed is human skin color-based face detection, which filters for skin-colored regions and finds facial parts within those regions. Advantages include speed and independence from training data, while disadvantages include sensitivity to lighting and accessories.
A Neural Network Approach to Deep-Fake Video DetectionIRJET Journal
This document presents a neural network approach for detecting deepfake videos. Deepfakes are videos generated using artificial intelligence that can make people appear to say or do things they did not actually say or do. The proposed system uses a convolutional neural network (CNN) to extract frame-level features from videos, which are then used to train a recurrent neural network (RNN) to classify whether a video has been manipulated or not. The system aims to detect inconsistencies in deepfake videos caused during their creation. It is tested on several public deepfake datasets and shows potential for achieving competitive results in detecting such manipulated media.
This is a presentation for Brandeis International Business School's Big Data II course about newer technologies using artificial intelligence, mainly the recently trendy Deepfake.
Although manipulations of visual and auditory media are as old as the media themselves, the recent entrance of deepfakes has marked a turning point in the creation of fake content. Powered by latest technological advances in AI and machine learning, they offer automated procedures to create fake content that is harder and harder to detect to human observers. The possibilities to deceive are endless, including manipulated pictures, videos and audio, that will have large societal impact. Because of this, organizations need to understand the inner workings of the underlying techniques, as well as their strengths and limitations. This article provides a working definition of deepfakes together with an overview of the underlying technology. We classify different deepfake types: photo (face- and body-swapping), audio (voice-swapping, text to speech), video (face-swapping, face-morphing, full body puppetry) and audio & video (lip-synching), and identify risks and opportunities to help organizations think about the future of deepfakes. Finally, we propose the R.E.A.L. framework to manage deepfake risks: Record original content to assure deniability, Expose deepfakes early, Advocate for legal protection and Leverage trust to counter credulity. Following these principles, we hope that our society can be more prepared to counter the deepfake tricks as we appreciate its treats.
Deepfakes are synthetic media that uses artificial intelligence to realistically manipulate images and videos by replacing a person's face with another. The term is a combination of "deep learning" and "fake". While deepfake technology was initially developed for entertainment purposes like special effects, it can also be used to impersonate people, create realistic simulations for training, and generate fake content for social media. However, there are disadvantages like using deepfakes for blackmail, spreading misinformation, and lack of authenticity which is why regulation of this technology is important.
SSII2021 [SS2] Deepfake Generation and Detection – An Overview (ディープフェイクの生成と検出)SSII
This document provides an overview of deepfake generation and detection. It begins with an introduction to the author and their background and research interests. The rest of the document is outlined as follows: definitions of deepfakes, various deepfake generation techniques including face synthesis, manipulation, reenactment and swapping, and an overview of deepfake detection methods including commonly used datasets, image-based and video-based detection approaches.
The “deepfake” phenomenon — using machine learning to generate synthetic video, audio and text content — is an ominous example of how quickly new technologies can be diverted from their original purposes. Month by month, it is becoming easier and cheaper to create fakes that are increasingly difficult to distinguish from genuine artefacts.
This document describes various algorithms used to build a facial emotion recognition system, including Haar cascade, HOG, Eigenfaces, and Fisherfaces. It explains how each algorithm works, such as how Haar cascade detects facial features and HOG extracts histograms of gradients. The system is trained on the CK+ dataset and uses Eigenface and Fisherface classifiers to classify emotions, achieving higher accuracy (86.54%) with Fisherfaces. It provides code snippets of key steps like cropping, resizing images, splitting data, and predicting emotions.
Face Detection and Recognition System (FDRS) is a physical characteristics recognition technology, using the inherent physiological features of humans for ID recognition. The technology does not need to be carried about and will not be lost, so it is convenient and safe for use
This document describes a deep learning algorithm to classify videos as deepfakes or authentic. It discusses deepfakes, how they are created, the system architecture including data preprocessing, a ResNext-50 model architecture with LSTM and training workflow. Results show models trained on different datasets and frame sequences achieving accuracies from 84% to 98%. The project uses PyTorch and Django with Google Cloud Platform for computing.
This document describes a project to build a convolutional neural network (CNN) model to recognize six basic human emotions (angry, fear, happy, sad, surprise, neutral) from facial expressions. The CNN architecture includes convolutional, max pooling and fully connected layers. Models are trained on two datasets - FERC and RaFD. Experimental results show that Model C achieves the best testing accuracy of 71.15% on FERC and 63.34% on RaFD. Visualizations of activation maps and a prediction matrix are provided to analyze the model's performance and confusions between emotions. A live demo application is also developed using OpenCV to demonstrate real-time emotion recognition from video frames.
Presentation for the Berlin Computer Vision Group, December 2020 on deep learning methods for image segmentation: Instance segmentation, semantic segmentation, and panoptic segmentation.
This document discusses face recognition systems and the use of artificial neural networks for face recognition. It describes the basic steps in a face recognition system as face detection, alignment, feature extraction, and matching. Two types of neural networks that can be used for recognition are described - Radial Basis Function Networks and Back Propagation Networks. RBF Networks have an input, hidden, and output layer while BPN uses backpropagation of errors to adjust weights. The document also outlines some applications of face recognition systems such as ID verification and criminal investigations.
This document provides an overview of face recognition technology. It discusses 2D and 3D facial recognition, how the technology works by measuring facial features to create a unique face print, hardware and software requirements, advantages like identifying repeat offenders, and applications in security, multimedia, and law enforcement. The conclusion states that while progress has been made, continued work is needed to develop more accurate systems.
Computer vision is a field of artificial intelligence that uses digital images and deep learning to teach machines to interpret and understand visual input. Early experiments in computer vision in the 1950s used neural networks to detect edges and classify simple shapes, while the 1970s saw the first commercial application in optical character recognition. Today, computer vision can perform tasks like facial recognition, object detection in images and video, and image segmentation, classification, and analysis that rival and exceed human visual abilities. Computer vision works by acquiring an image, processing it through machine learning models, and understanding what is depicted to take appropriate actions.
This document provides an overview of Kalyan Acharjya's proposed work on face recognition for his M.Tech dissertation. It discusses conducting literature research on existing face recognition techniques, identifying challenges in real-time applications, and exploring standard face image databases. The presentation covers topics such as how face recognition works, applications, and concludes with plans to modify existing algorithms and compare results to related work to enhance recognition rates.
Face detection and recognition using surveillance camera2 editedSantu Chall
The document discusses face detection and recognition technology using surveillance cameras. It describes how face recognition systems work by detecting 80 nodal points on the face and creating a "face print" code based on distance measurements. The document outlines general face recognition steps including face detection, normalization, and identification. It discusses advantages like convenience and passive identification, and disadvantages like inability to distinguish identical twins. Potential applications described include security, law enforcement, immigration, and banking. The document proposes a 12-week project plan to develop a face recognition system prototype.
This document presents information on face detection techniques. It discusses image segmentation as a preprocessing step for face detection. Some common segmentation methods are thresholding, edge-based segmentation, and region-based segmentation. Face detection can be classified as implicit/pattern-based or explicit/knowledge-based. Implicit methods use techniques like templates, PCA, LDA, and neural networks, while explicit methods exploit cues like color, motion, and facial features. One method discussed is human skin color-based face detection, which filters for skin-colored regions and finds facial parts within those regions. Advantages include speed and independence from training data, while disadvantages include sensitivity to lighting and accessories.
A Neural Network Approach to Deep-Fake Video DetectionIRJET Journal
This document presents a neural network approach for detecting deepfake videos. Deepfakes are videos generated using artificial intelligence that can make people appear to say or do things they did not actually say or do. The proposed system uses a convolutional neural network (CNN) to extract frame-level features from videos, which are then used to train a recurrent neural network (RNN) to classify whether a video has been manipulated or not. The system aims to detect inconsistencies in deepfake videos caused during their creation. It is tested on several public deepfake datasets and shows potential for achieving competitive results in detecting such manipulated media.
People Monitoring and Mask Detection using Real-time video analyzingvivatechijri
People Counting and mask detection based on video is an important field in a Computer Vision. There is growing interest in video-based solutions for people monitoring and counting in business and security applications using Computer Vision technology. It has been effectively used in many Artificial Intelligence fields. Compareing to normal sensor based solutions the one with video based allows more flexible performance, improved functionalities with lower costs. The system with people counter program requires more processing because that deals with real-time video, so this particular proposed technique converts a color image into binary in order to minimize data of image. Reducing processing time is an important term in Software Engineering to build a good working system. People counting methods based on head detection and tracking to evaluate the total number of people who move under an overhead camera and check whether that people are wearing a mask or not. There basically four main features in this proposed system: People counting, Mask detection, Alarm alert and Scan ID. Based on tracking of head, this method uses the crossing-line judgment to determine whether the particular head object will get counted or not to be counted. The two main challenges overcome in this system are: tough estimation of the background scene and the number of persons in merge split scenarios. A technique for masked face detection using three different steps of estimating eye line detection, facial part detection and eye detection is used in this system. On exceeding the count of people or in case mask is not worn then alarm gets alerted
Covid Mask Detection and Social Distancing Using Raspberry piIRJET Journal
This document describes a system that uses computer vision and machine learning to detect if individuals are wearing masks and maintaining proper social distancing in public places. The system uses a Raspberry Pi connected to a USB camera to take photos and video. Convolutional neural network models like CNN and YOLO are used to analyze the images, detect faces, and determine if masks are being worn correctly. If individuals are not wearing masks or social distancing, the system will provide an alert or sound from a connected speaker. The goal is to help enforce mask and distancing guidelines without needing human monitoring, in order to reduce virus spread during the COVID-19 pandemic.
A survey of deepfakes in terms of deep learning and multimedia forensicsIJECEIAES
Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfake detection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection of deepfakes, and iii) finally how in the future incorporating both deep learning technology and tools for forensics can increase the efficiency of deepfakes detection.
Fast discrimination of fake video manipulationIJECEIAES
Deepfakes have become possible using artificial intelligence techniques, replacing one person’s face with another person’s face (primarily a public figure), making the latter do or say things he would not have done. Therefore, contributing to a solution for video credibility has become a critical goal that we will address in this paper. Our work exploits the visible artifacts (blur inconsistencies) which are generated by the manipulation process. We analyze focus quality and its ability to detect these artifacts. Focus measure operators in this paper include image Laplacian and image gradient groups, which are very fast to compute and do not need a large dataset for training. The results showed that i) the Laplacian group operators, as a value, may be lower or higher in the fake video than its value in the real video, depending on the quality of the fake video, so we cannot use them for deepfake detection and ii) the gradient-based measure (GRA7) decreases its value in the fake video in all cases, whether the fake video is of high or low quality and can help detect deepfake.
Fake Video Creation and Detection: A ReviewIRJET Journal
This document summarizes research on fake video creation and detection using deep learning techniques. It discusses how advances in deep learning, particularly generative adversarial networks (GANs), have made it easier to generate realistic fake videos but also pose risks if misused. The document reviews methods for creating fake videos, such as face swapping and face reenactment using autoencoders, as well as methods for detecting fake videos by examining visual artifacts in frames or temporal inconsistencies across frames using classifiers like CNNs. Overall, the document provides an overview of the state of deepfake video generation and detection.
Broadcasting Forensics Using Machine Learning Approachesijtsrd
Broadcasting forensic is the practice of using scientific methods and techniques to analyse and authenticate Multimedia content. Over the past decade, consumer grade imaging sensors have become increasingly prevalent, generating vast quantities of images and videos that are used for various public and private communication purposes. Such applications include publicity, advocacy, disinformation, and deception, among others. This paper aims to develop tools that can extract knowledge from these visuals and comprehend their provenance. However, many images and videos undergo modification and manipulation before public release, which can misrepresent the facts and deceive viewers. To address this issue, we propose a set of forensics and counter forensic techniques that can help establish the authenticity and integrity of Multimedia content. Additionally, we suggest ways to modify the content intentionally to mislead potential adversaries. Our proposed tools are evaluated using publicly available datasets and independently organized challenges. Our results show that the forensics and counter forensic techniques can accurately identify manipulated content and can help restore the original image or video. Furthermore, in this paper demonstrate that the modified content can successfully deceive potential adversaries while remaining undetected by state of the art forensic methods. Amit Kapoor | Prof. Vinod Mahor "Broadcasting Forensics Using Machine Learning Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd57545.pdf Paper URL: https://www.ijtsrd.com.com/engineering/computer-engineering/57545/broadcasting-forensics-using-machine-learning-approaches/amit-kapoor
A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced
with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often
used maliciously. A common usage is altering videos of prominent political figures and celebrities. These
deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes
can be very realistic, and when used in this way, can spread panic and even influence elections and
political opinions. There are many deepfake detection strategies currently in use but finding the most
comprehensive and universal method is critical. So, in this survey we will address the problems of
malicious deepfake creation and the lack of universal deepfake detection methods. Our objective is to
survey and analyze a variety of current methods and advances in the field of deepfake detection.
A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced
with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often
used maliciously. A common usage is altering videos of prominent political figures and celebrities. These
deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes
can be very realistic, and when used in this way, can spread panic and even influence elections and
political opinions. There are many deepfake detection strategies currently in use but finding the most
comprehensive and universal method is critical. So, in this survey we will address the problems of
malicious deepfake creation and the lack of universal deepfake detection methods. Our objective is to
survey and analyze a variety of current methods and advances in the field of deepfake detection.
This document provides an overview of facial emotion recognition techniques. It discusses face detection methods like Viola-Jones, multi-task cascaded convolutional neural networks (CNN), and R-CNN. For face recognition, it covers linear approaches like Eigenfaces, Fisherfaces, and Marginfaces. It also discusses CNN and HOG methods for emotion detection, like AlexNet CNN and HOG-ESRs. Popular datasets for evaluation include CK+, JAFFE, and FER2013. The document surveys key algorithms and approaches for face detection, recognition, and emotion detection.
Human motion is fundamental to understanding behaviour. In spite of advancement on single image 3 Dimensional pose and estimation of shapes, current video-based state of the art methods unsuccessful to produce precise and motion of natural sequences due to inefficiency of ground-truth 3 Dimensional motion data for training. Recognition of Human action for programmed video surveillance applications is an interesting but forbidding task especially if the videos are captured in an unpleasant lighting environment. It is a Spatial-temporal feature-based correlation filter, for concurrent observation and identification of numerous human actions in a little-light environment. Estimated the presentation of a proposed filter with immense experimentation on night-time action datasets. Tentative results demonstrate the potency of the merging schemes for vigorous action recognition in a significantly low light environment.
System for Detecting Deepfake in Videos – A SurveyIRJET Journal
This document provides a survey of systems for detecting deepfake videos. It begins with an abstract discussing how freely available deep learning software can generate highly realistic fake content, and the need to develop detection methods to mitigate the negative impacts. The document then reviews several techniques for creating face-based manipulated videos, including face swapping, attribute manipulation, and expression transfer. It also examines popular deepfake generation tools like FaceSwap, Deepfakes, and Face2Face. Several datasets used for deepfake detection are presented, and detection methods based on convolutional neural networks, recurrent neural networks, and generative adversarial networks are explored. Key deep learning techniques for both generating and detecting deepfakes are summarized.
Review on Arduino-Based Face Mask Detection SystemIRJET Journal
1. The document reviews an Arduino-based face mask detection system that uses deep learning and computer vision techniques to detect faces and determine if a face mask is being worn in images and video in real-time.
2. The system is designed to help prevent the spread of COVID-19 by only allowing people wearing face masks to enter certain locations. It works by detecting faces with a camera or video feed, classifying whether a mask is detected on the face using a trained model, and then sends a signal to an Arduino device that can control access points like doors.
3. The methodology involves face acquisition from images/video, classification of faces as with or without a mask using a deep learning model, and
Deep Unified Model for Intrusion Detection Based on Convolutional Neural NetworkYogeshIJTSRD
Indian army has always been subject to military attacks from neighbouring countries. Despite many surveillance devices and border security forces, the enemy finds a way to infiltrate deep into our borders. This is mainly because even now the surveillance in India is largely human assisted. Therefore this automated surveillance can authenticate the authorized persons and alert everyone when an enemy intrusion is detected. In this, we proposed an automated surveillance system that tackles the predicament of recognition of faces subject to different real time scenarios. This model incorporates a camera that captures the input image, an algorithm to detect a face from the input image, recognize the face using a convolution neural network along with transfer learning method, and verifies the detected person. The authorized person’s name and details are stored in CSV format and then into the database. In case of any unauthorized persons face is detected the image of the intruder along with time is stored in the database and warning signal is also given to alert the surrounding members in case of intrusion detection. Dhanu Shree D | Fouzia Fathima A | Madhumita B | Akila G | Thulasiram S "Deep Unified Model for Intrusion Detection Based on Convolutional Neural Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39976.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39976/deep-unified-model-for-intrusion-detection-based-on-convolutional-neural-network/dhanu-shree-d
This document discusses the development of a face mask detection system using YOLOv4. The system uses a deep learning model with YOLOv4 to detect faces in real-time video and determine if each person is wearing a mask or not. It is trained on images of faces with and without masks. The model uses CSPDarknet53 as the backbone network and PANet for feature aggregation. It is implemented with OpenCV and a Python GUI for a user interface. The goal is to help enforce mask mandates and alert authorities if too many people in an area are not wearing masks.
CRIMINAL IDENTIFICATION FOR LOW RESOLUTION SURVEILLANCEvivatechijri
Criminal Identification System allows the user to identify a certain criminal based on their biometrics. With advancements in security technology, CCTV cameras have been installed in many public and private areas to provide surveillance activities. The CCTV footage becomes crucial for understanding of the criminal activities that take place and to detect suspects. Additionallywhen a criminal is found it is difficult to locate and track him with just his image if he is on the run. Currently this procedure consists of finding such people in CCTV surveillance footage manually which is time consuming. It is also a tedious process as the resolution for such CCTV cameras is quite low. As a solution to these issues, the proposed system is developed to go through real time surveillance footage, detect and recognize the criminals based on reference datasets of criminals. The use of facial recognition for identifying criminals proves to bebeneficial. Once the best match is found the real time cropped image of the recognized criminal is saved which can be accessed by authorized officials for locating and tracking criminals or for further investigative use.
This document presents a study on sign language recognition using computer vision techniques. It aims to develop a system that can identify characters and numbers in Indian Sign Language (ISL) using convolutional neural networks. ISL uses both hands to communicate unlike American Sign Language which uses a single hand. The system creates a dataset of ISL gestures and trains a CNN model on it. It then tests the ability of the trained model to accurately predict numbers from 1 to 10 and letters from A to Z when presented with new sign language inputs. The model achieves over 90% accuracy on test data, providing an effective way to translate ISL signs and bridge communication between deaf/mute and non-signing individuals.
Face and liveness detection with criminal identification using machine learni...IAESIJAI
In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.
IRJET- Deep Learning Based Card-Less Atm Using Fingerprint And Face Recogniti...IRJET Journal
This document summarizes a proposed system for a cardless ATM that uses fingerprint and face recognition for authentication instead of an ATM card. The system would use CNN models for face recognition and minutiae feature extraction for fingerprint recognition. It provides background on deep learning techniques and discusses related work applying deep learning for tasks like face recognition, fingerprint spoofing detection, and improving machine learning algorithms. The proposed cardless ATM system aims to address security issues with the current card-based system and allow multiple family members to access the same account from different locations using their biometrics instead of an ATM card.
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Understanding the Impact and Challenges of Corona Crisis on Education Sector...vivatechijri
n the second week of March 2020, governments of all states in a country suddenly declared
shutting down of all colleges and schools for a temporary period of time as an immediate measure to stop the
spread of pandemic that is of novel corona virus. As the days pass by almost close to a month with no certainty
when they will again reopen. Due to pandemic like this an alarm bells have started sounding in the field of
education where a huge impact can be seen on teaching and learning process as well as on the entire education
sector in turn. The pandemic disruption like this is actually gave time to educators of today to really think about
the sector. Through the present research article, the author is highlighting on the possible impact of
coronavirus on education sector with the future challenges for education sector with possible suggestions.
LEADERSHIP ONLY CAN LEAD THE ORGANIZATION TOWARDS IMPROVEMENT AND DEVELOPMENT vivatechijri
This document discusses the importance of leadership in leading an organization towards improvement and development. It states that leadership is responsible for providing a clear vision and strategy to successfully achieve that vision. Effective leadership can impact the success of an organization by controlling its direction and motivating employees. Leadership is different from traditional management in that it guides employees towards organizational goals through open communication and motivation, rather than simply directing work. The paper concludes that only leadership can lead an organization to change according to its evolving environment, while management may simply follow old rules. Leadership is key to adapting to new market needs and trends.
The topic of assignment is a critical problem in mathematics and is further explored in the real
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Structural and Morphological Studies of Nano Composite Polymer Gel Electroly...vivatechijri
The document summarizes research on a nano composite polymer gel electrolyte containing SiO2 nanoparticles. Key points:
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Theoretical study of two dimensional Nano sheet for gas sensing applicationvivatechijri
This study is focus on various two dimensional material for sensing various gases with theoretical
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METHODS FOR DETECTION OF COMMON ADULTERANTS IN FOODvivatechijri
Food is essential forliving. Food adulteration deceives consumers and can endanger their health. The
purpose of this document is to list common food adulterant methods commonly found in India. An adulterant is
a substance found in other substances such as food, cosmetics, pharmaceuticals, fuels, or other chemicals that
compromise the safety or effectiveness of that substance. The addition of adulterants is called adulteration. The
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The novel ideas of being a entrepreneur is a key for everyone to get in the hustle, but developing a
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Innovation for supporting prosperity has for quite some time been a focus on numerous orders, including PC science, brain research, and human-PC connection. In any case, the meaning of prosperity isn't continuously clear and this has suggestions for how we plan for and evaluate advances that intend to cultivate it. Here, we talk about current meanings of prosperity and how it relates with and now and then is a result of self-amazing quality. We at that point center around how innovations can uphold prosperity through encounters of self-amazing quality, finishing with conceivable future bearings.
An Alternative to Hard Drives in the Coming Future:DNA-BASED DATA STORAGEvivatechijri
Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up, there emerges a requirement for a storage medium with high capacity, high storage density, and possibility to face up to extreme environmental conditions. According to a research in 2018, every minute Google conducted 3.88 million searches, other people posted 49,000 photos on Instagram, sent 159,362,760 e-mails, tweeted 473,000 times and watched 4.33 million videos on YouTube. In 2020 it estimated a creation of 1.7 megabytes of knowledge per second per person globally, which translates to about 418 zettabytes during a single year. The magnetic or optical data-storage systems that currently hold this volume of 0s and 1s typically cannot last for quite a century. Running data centres takes vast amounts of energy. In short, we are close to have a substantial data-storage problem which will only become more severe over time. Deoxyribonucleic acid (DNA) are often potentially used for these purposes because it isn't much different from the traditional method utilized in a computer. DNA’s information density is notable, 215 petabytes or 215 million gigabytes of data can be stored in just one gram of DNA. First we can encode all data at a molecular level and then store it in a medium that will last for a while and not become out-dated just like floppy disks. Due to the improved techniques for reading and writing DNA, a rapid increase is observed in the amount of possible data storage in DNA.
The usage of chatbots has increased tremendously since past few years. A conversational interface is an interface that the user can interact with by means of a conversation. The conversation can occur by speech but also by text input. When a chatty interface uses text, it is also described as a chatbot or a conversational medium. During this study, the user experience factors of these so called chatbots were investigated. The prime objective is “to spot the state of the art in chatbot usability and applied human-computer interaction methodologies, to research the way to assess chatbots usability". Two sorts of chatbots are formulated, one with and one without personalisation factors. the planning of this research may be a two-by-two factorial design. The independent variables are the two chatbots (unpersonalised versus personalised) and thus the speci?c task or goal the user are ready to do with the chatbot within the ?nancial ?eld (a simple versus a posh task). The results are that there was no noteworthy interaction effect between personalisation and task on the user experience of chatbots. A signi?cant di?erence was found between the two tasks with regard to the user experience of chatbots, however this variation wasn't because of personalisation.
The Smart glasses Technology of wearable computing aims to identify the computing devices into today’s world.(SGT) are wearable Computer glasses that is used to add the information alongside or what the wearer sees. They are also able to change their optical properties at runtime.(SGT) is used to be one of the modern computing devices that amalgamate the humans and machines with the help of information and communication technology. Smart glasses is mainly made up of an optical head-mounted display or embedded wireless glasses with transparent heads- up display or augmented reality (AR) overlay in it. In recent years, it is been used in the medical and gaming applications, and also in the education sector. This report basically focuses on smart glasses, one of the categories of wearable computing which is very popular presently in the media and expected to be a big market in the next coming years. It Evaluate the differences from smart glasses to other smart devices. It introduces many possible different applications from the different companies for the different types of audience and gives an overview of the different smart glasses which are available presently and will be available after the next few years.
Future Applications of Smart Iot Devicesvivatechijri
With the Internet of Things (IoT) bit by bit creating as the resulting time of the headway of the Internet, it gets critical to see the diverse expected zones for the utilization of IoT and the research challenges that are connected with these applications going from splendid savvy urban areas, to medical care administrations, shrewd farming, collaborations and retail. IoT is needed to attack into for all expectations and purposes for all pieces of our day-to-day life. Despite the fact that the current IoT enabling advancements have immensely improved in the continuous years, there are so far different issues that require attention. Since the IoT ideas results from heterogeneous advancements, many examination difficulties will arise. In like manner, IoT is planning for new components of exploration to be finished. This paper presents the progressing headway of IoT advancements and inspects future applications.
Cross Platform Development Using Fluttervivatechijri
Today the development of cross-platform mobile application has under the state of compromise. The developers are not willing to choose an alternative of either building the similar app many times for many operating systems or to accept a lowest common denominator and optimal solution that will going to trade the native speed, accuracy for portability. The Flutter is an open-source SDK for creating high-performance, high fidelity mobile apps for the development of iOS and Android. Few significant features of flutter are - Just-in-time compilation (JIT), Ahead- of-time compilation (AOT compilation) into a native (system-dependent) machine code so that the resulting binary file can execute natively. The Flutter’s hot reload functionality helps us to understand quickly and easily experiment, build UIs, add features, and fix bugs. Hot reload works by injecting updated source code files into the running Dart Virtual Machine (VM). With the help of Flutter, we believe that we would be having a solution that gives us the best of both worlds: hardware accelerated graphics and UI, powered by native ARM code, targeting both popular mobile operating systems.
The Internet, today, has become an important part of our lives. The World Wide Web that was once a small and inaccessible data storage service is now large and valuable. Current activities partially or completely integrated into the physical world can be made to a higher standard. All activities related to our daily life are mapped and linked to another business in the digital world. The world has seen great strides in the Internet and in 3D stereoscopic displays. The time has come to unite the two to bring a new level of experience to the users. 3D Internet is a concept that is yet to be used and requires browsers to be equipped with in-depth visualization and artificial intelligence. When this material is included, the Internet concept of material may become a reality discussed in this paper. In this paper we have discussed the features, possible setting methods, applications, and advantages and disadvantages of using the Internet. With this paper we aim to provide a clear view of 3D Internet and the potential benefits associated with this obviously cost the amount of investment needed to be used.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
The study LiFi (Light Fidelity) demonstrates about how can we use this technology as a medium of communication similar to Wifi . This is the latest technology proposed by Harold Haas in 2011. It explains about the process of transmitting data with the help of illumination of an Led bulb and about its speed intensity to transmit data. Basically in this paper, author will discuss about the technology and also explain that how we can replace from WiFi to LiFi . WiFi generally used for wireless coverage within the buildings while LiFi is capable for high intensity wireless data coverage in limited areas with no obstacles .This research paper represents introduction of the Lifi technology,performance,modulation and challenges. This research paper can be used as a reference and knowledge to develop some of LiFitechnology.
Social media platform and Our right to privacyvivatechijri
The advancement of Information Technology has hastened the ability to disseminate information across the globe. In particular, the recent trends in ‘Social Networking’ have led to a spark in personally sensitive information being published on the World Wide Web. While such socially active websites are creative tools for expressing one’s personality it also entails serious privacy concerns. Thus, Social Networking websites could be termed a double edged sword. It is important for the law to keep abreast of these developments in technology. The purpose of this paper is to demonstrate the limits of extending existing laws to battle privacy intrusions in the Internet especially in the context of social networking. It is suggested that privacy specific legislation is the most appropriate means of protecting online privacy. In doing so it is important to maintain a balance between the competing right of expression, the failure of which may hinder the reaping of benefits offered by Internet technology
THE USABILITY METRICS FOR USER EXPERIENCEvivatechijri
THE USABILITY METRICS FOR USER EXPERIENCE was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as THE USABILITY METRICS FOR USER EXPERIENCE that is GFS. THE USABILITY METRICS FOR USER EXPERIENCE is one of the largest file system in operation. Generally THE USABILITY METRICS FOR USER EXPERIENCE is a scalable distributed file system of large distributed data intensive apps. In the design phase of THE USABILITY METRICS FOR USER EXPERIENCE, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. THE USABILITY METRICS FOR USER EXPERIENCE also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, THE USABILITY METRICS FOR USER EXPERIENCE is highly available, replicas of chunk servers and master exists.
Google File System was innovatively created by Google engineers and it is ready for production in record time. The success of Google is to attributed the efficient search algorithm, and also to the underlying commodity hardware. As Google run number of application then Google’s goal became to build a vast storage network out of inexpensive commodity hardware. So Google create its own file system, named as Google File System that is GFS. Google File system is one of the largest file system in operation. Generally Google File System is a scalable distributed file system of large distributed data intensive apps. In the design phase of Google file system, in which the given stress includes component failures , files are huge and files are mutated by appending data. The entire file system is organized hierarchically in directories and identified by pathnames. The architecture comprises of multiple chunk servers, multiple clients and a single master. Files are divided into chunks, and that is the key design parameter. Google File System also uses leases and mutation order in their design to achieve atomicity and consistency. As of there fault tolerance, Google file system is highly available, replicas of chunk servers and master exists.
A Study of Tokenization of Real Estate Using Blockchain Technologyvivatechijri
Real estate is by far one of the most trusted investments that people have preferred, being a lucrative investment it provides a steady source of income in the form of lease and rents. Although there are numerous advantages, one of the key downsides of real estate investments is lack of liquidity. Thus, even though global real estate investments amount to about twice the size of investments in stock markets, the number of investors in the real estate market is significantly lower. Block chain technology has real potential in addressing the issues of liquidity and transparency, opening the market to even retail investors. Owing to the functionality and flexibility of creating Security Tokens, which are backed by real-world assets, real estate can be made liquid with the help of Special Purpose Vehicles. Tokens of ERC 777 standard, which represent fractional ownership of the real estate can be purchased by an investor and these tokens can also be listed on secondary exchanges. The robustness of Smart Contracts can enable the efficient transfer of tokens and seamless distribution of earnings amongst the investors. This work describes Ethereum blockchainbased solutions to make the existing Real Estate investment system much more efficient.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Gas agency management system project report.pdfKamal Acharya
The project entitled "Gas Agency" is done to make the manual process easier by making it a computerized system for billing and maintaining stock. The Gas Agencies get the order request through phone calls or by personal from their customers and deliver the gas cylinders to their address based on their demand and previous delivery date. This process is made computerized and the customer's name, address and stock details are stored in a database. Based on this the billing for a customer is made simple and easier, since a customer order for gas can be accepted only after completing a certain period from the previous delivery. This can be calculated and billed easily through this. There are two types of delivery like domestic purpose use delivery and commercial purpose use delivery. The bill rate and capacity differs for both. This can be easily maintained and charged accordingly.
1. VIVA-Tech International Journal for Research and Innovation
ISSN(Online): 2581-7280
Volume 1, Issue 4 (2021)
Article No. X
PP XX-XX
VIVA Institute of Technology
9th
National Conference on Role of Engineers in Nation Building – 2021 (NCRENB-2021)
1
www.viva-technology.org/New/IJRI
DEEPFAKE DETECTION TECHNIQUES: A REVIEW
Neeraj Guhagarkar1
, Sanjana Desai2
Swanand Vaishyampayan3
, Ashwini Save4
1
Department Computer Engineering, Mumbai University, MUMBAI
2
Department Computer Engineering, Mumbai University, MUMBAI
3
Department Computer Engineering, Mumbai University, MUMBAI
4
Department Computer Engineering, Mumbai University, MUMBAI
Abstract : Noteworthy advancements in the field of deep learning have led to the rise of highly realistic AI
generated fake videos, these videos are commonly known as Deepfakes. They refer to manipulated videos, that
are generated by sophisticated AI, that yield formed videos and tones that seem to be original. Although this
technology has numerous beneficial applications, there are also significant concerns about the disadvantages of
the same. So there is a need to develop a system that would detect and mitigate the negative impact of these AI
generated videos on society. The videos that get transferred through social media are of low quality, so the
detection of such videos becomes difficult. Many researchers in the past have done analysis on Deepfake
detection which were based on Machine Learning, Support Vector Machine and Deep Learning based
techniques such as Convolution Neural Network with or without LSTM .This paper analyses various techniques
that are used by several researchers to detect Deepfake videos.
Keywords - Convolutional Neural Networks, Deepfake Detection, Long Short Term Memory , Super
Resolution, Facial Forgery.
I. INTRODUCTION
Remarkable improvements in the field of Deep Learning have led to the growth of Deepfake videos. With the
help of Deep Learning architectures such as Generative Adversarial Neural Networks (GANs) and autoencoders
and a considerable amount of footage of a target subject, anyone can create such convincing fake videos [4].
Head Puppetry, Face swapping and Lip-syncing are the 3 major types of Deepfake videos [3]. This technique
has provided potential harm to society. For instance, Indian journalist Rana Ayyub had become the victim of a
sinister Deepfake plot. A fake pornographic video that showed her in it was shared on social media platforms
such as Twitter and WhatsApp [25]. Therefore, there is an urgent need for researchers to develop a system that
would detect such Deepfake videos. This paper focuses on various techniques like Machine Learning techniques
based on Support Vector Machine(SVM) [1][16][19][22], Deep learning techniques like Convolution Neural
Network(CNN) [10][14][2], CNN with SVM [3], CNN with Long Short Term Memory(LSTM)
[4][6][7][11][12][14] and Recurrent Neural Network(RNN) [20]. Also, many different approaches such as
considering the manipulation in the background color [19], exposing inconsistent headposes [1], to detect
Deepfake videos.
II. LITERATURE SURVEY
Xin Yang, et. al. [1] have proposed a system to detect Deepfake using inconsistent headposes. Algorithms
used in the previous model create the face of different persons without changing the original expressions hence
creating mismatched facial landmarks. The landmark locations of few false faces often vary from those of the
real faces, as a consequence of interchanging faces in the central face region in the DeepFake process.The
difference in the distribution of the cosine distances of the two head orientation vectors for real and Deepfakes
suggest that they can be differentiated based on this cue. It uses the DLib package for face detection and to
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extract 68 facial landmarks. The standard facial 3D model is created with OpenFace2, and then difference is
calculated.The proposed system uses UADFV dataset. Trained SVM classifier with Radial basis function (RBF)
kernels on the training data is used. Area Under ROC (AUROC) of 0.89, is achieved by the SVM classifier on
the UADFV dataset. The crucial point that can be inferred from this paper is the focus on how the Deepfakes are
generated by splicing a synthesized face region into the original image, and how it can also use 3D pose
estimation for detecting synthesized videos.
Rohita Jagdale, et. al. [2] have proposed a novel algorithm NA-VSR for Super resolution. The algorithm
initially reads the low resolution video and converts it into frames. Then the median filter is used to remove
unwanted noise from video. The pixel density of the image is increased by bicubic interpolation technique. Then
Bicubic transformation and image enhancement is done for mainly resolution enhancement. After these steps the
design metric is computed. It uses the output peak signal-to-noise ratio (PSNR) and structural similarity index
method (SSIM) to determine the quality of image. Peak signal-to-noise ratio and structural similarity index
method parameters are computed for NA-VSR and compared with previous methods. Peak signal to noise ratio
(PSNR) of the proposed method is improved by 7.84 dB, 6.92 dB, and 7.42 dB as compared to bicubic,
SRCNN, and ASDS respectively.
Siwei Lyu,[3] has surveyed various challenges and also discussed research opportunities in the field of
Deepfakes. One critical disadvantage of the current DeepFake generation methods is that they cannot produce
good details such as skin and facial hairs. This is due to the loss of information in the encoding step of
generation. Head puppetry involves copying the source person’s head and upper shoulder part and then pasting
it on the target person’s body , so that target appears to behave in a similar way as that of the source. The second
method is face swapping which swaps only the face of the source person with that of the target. It also keeps the
facial expressions unchanged. The third method is Lip syncing which is used to create a falsified video by only
manipulating the lip region so that the target appears to speak something that she/he does not speak in reality.
The detection methods are formulated as frame level binary classification problems. Out of the three widely
used detection methods, the first category considers inconsistencies exhibited in the physical/physiological
aspects in the DeepFake videos. The second algorithm makes use of the signal-level artifacts. Data driven is the
last category of Detection in this, it directly employs multiple types of DNNs trained on genuine and Fake
videos but captures only explicit artifacts. It also sheds some light on the limitations of these methods such as
quality of deepfake datasets, social media laundering, etc.
Digvijay Yadav, et. al. [4] have elaborated the working of the deepfake techniques along with how it can
swap faces with high precision. The Generative Adversarial Neural Networks (GANs) contain two neural
networks, the first is generator and other is discriminator. Generator neural networks create the fake images
from the given data set. On the other hand, discriminator neural networks evaluate the images which are
synthesized by the generator and check its authenticity. Deepfake are harmful because of cases like individual
character defamation and assassination, spreading fake news, threat to law enforcement agencies. For detection
of Deepfakes blinking of eyes can be considered as a feature. The limitations for making Deepfakes are the
requirement of large datasets, training and swapping is time consuming, similar faces and skin tones of people,
etc. Deepfake video detection can be done using recurrent neural networks. CNN is best known for its visual
recognition and if it is combined with LSTM it can easily detect changes in the frames and then this information
is used for detecting the DeepFakes. The paper suggests that Meso-4 and Mesoinception-4 architectures are
capable of detecting the Deepfake video with the accuracy of 95% to 98% on Face2Face dataset.
Irene Amerini, et. al. [5] have proposed a system to exploit possible inter-frame dissimilarities using the
optical flow technique. CNN classifiers make use of this clue as a feature to learn. The optical flow fields
calculated on two consecutive frames for an original video and the corresponding Deepfake one are pictured and
it can be noticed that the motion vectors around the chin in the real sequence are more vociferous in comparison
with those of the altered video that appear much smoother. This is used as a clue to help neural networks learn
properly. FaceForensics++ dataset was used, in that 720 videos were used for training, 120 videos for validation,
and 120 videos for testing. They used two neural networks VGG16 and ResNet 50. For Face2Face videos, VGG
gives detection accuracy of 81.61 % and ResNet50 gives detection accuracy of 75.46 %. The uniqueness of this
paper is the consideration of inter-frame dissimilarities, unlike other techniques which rely only on intra-frame
inconsistencies and how to overcome them using the optical flow based CNN method.
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Peng Chen, et. al. [6] have developed FSSPOTTER which is a unified framework, which can simultaneously
explore spatial and temporal information in the videos. The Spatial Feature Extractor (SFE) first divides the
videos into several consecutive clips, each of which contains a certain number of frames. SFE takes clips as
input and generates frame-level features. It uses convolution layers of Visual Geometry Group VGG16 with
batch normalization as the backbone network which extracts spatial features in the intra-frames. Also, the
superpixel-wise binary classification unit (SPBCU) is exploited to encourage the backbone network to extract
more features. The Temporal Feature Aggregator (TFG) deploys a Bidirectional LSTM to find the temporal
inconsistencies in the frame. Then, a fully connected layer and a softmax layer are exploited to compute the
probabilities of whether the clip is real or fake. FaceForensics++, DeepfakeTIMIT, UADFV and Celeb-DF are
used for the evaluation. FSSpotter takes a simple VGG16 as the backbone network and it is superior to Xception
by 2.2% for UADFV, 5.4% for Celeb-DF, and 4.4%, 2.0% for DeepfakeTIMIT HQ, LQ respectively.
Mohammed A. Younus, et. al. [7] have compared notable Deepfake detection methods. From various
methods, few techniques include Background Comparison, Temporal Pattern Analysis, Eye blinking, Facial
Artifacts, etc. The initial method uses Long Term Recurrent CNN (LRCN) to learn the temporal pattern of eye
blinking and the dataset used consists of 49 interview and presentation videos and their corresponding generated
Deepfakes. With the help RCN that unites convolutional network DenseNet and the gated recurrent unit cells
few temporal discrepancies across frames are explored in the background comparison technique. In this
FaceForensics++ dataset is used. The third approach is Temporal Pattern Analysis which uses Convolutional
Neural Network (CNN) to extract frame-level features and Long Short Term Memory (LSTM) for classification.
The dataset used for this method is a batch of 600 videos obtained from multiple websites. In the fourth method,
Artifacts are discovered using ResNet50 CNN models based on resolution inconsistency between the warped
face area and the surrounding context. The Mesoscopic Analysis uses two networks Meso-4 and MesoInception-
4 for determining the Deefakes. This study paper helped to understand various deepfake detection approaches
currently used and recommends how other features could be found that would help to detect Deepfakes more
efficiently.
Shivangi Aneja, et. al. [8] to overcome the challenge of zero and few-shot transfer they have proposed a
transfer learning-based approach, called Deep Distribution Transfer (DDT). The fundamental idea behind this
method was a new distribution-based loss formulation that can be efficiently equipped to span the gap between
domains of different facial forgery techniques or obscure datasets. The proposed method outperforms the
baselines by a significant margin in both zero-shot and few-shot learning. The model uses an ImageNet Large
Scale Visual Recognition Challenge ILSVRC 2012-pretrained ResNet-18 neural network. A 4.88% higher
detection efficiency for zero-shot and 8.38% for the few-shot case transferred is achieved. The suggested
method tries to generalize the forgery detection techniques by using zero and a few shot transfers. Hence, a
similar approach can be followed to broaden the scope of the project for the detection of forgery on several
datasets.
XTao, et. al. [9] have proposed a system that emphasizes the fact that to achieve better results, proper frame
alignment and motion compensation needs to be done. The authors have introduced a sub-pixel motion
compensation layer (SPMC) layer in a CNN framework. Along with FlowNet-S CNN, frame alignment and
motion compensation is achieved using motion compensation transformer (MCT) module. Also, they have
collected 975 sequences from high-quality 1080p HD video clips publically available on the internet and
downsampled the original frames to 540 × 960 pixels. The proposed method has a Peak signal-to-noise ratio
(PSNR) of 36.71 and a Structural Similarity Index (SSIM) value of 0.96 which is better than that of the
previously proposed SRCNN. This paper provides an insight into how to organize multiple frame inputs for
getting better results. Also, it gives a foreknowledge about how data is to be sampled before feeding it to the
CNN model.
Jin Yamanaka, et. al. [10] have asserted that mostly single image Super resolution is used for medical
systems, surveillance systems. But the computational power required for the system is very high so it cannot be
used for smaller devices. But as the need for super resolution is increasing, they have proposed a different way
which will reduce the deep CNN computational power by 10 to 100 times still maintaining higher accuracy. The
no of layers in the neural network used so far has 30 layers but with the authors' system, the no of layers will
reduce to 11 only hence decreasing the computational power on a large scale. The computational power of the
3*3 matrix will be 9 times the computational power of nine 1*1 matrix. Datasets used have a total of 1,164
training images and a total size of 435 MB. It focuses on a super resolution algorithm and proposes a different
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way to perform super resolution with reduced space complexity and reduced computational power. The system
used in the paper also has a reduced number of neural layers which is the significant takeaway from this paper.
Even if they are reducing the neural layers down to 11 from 30, the proposed system does not lose its accuracy
but the accuracy was only incremented as per the shown outcomes in the paper.
David guera, et. al. [11] have demonstrated how Deepfake videos are created and how they can be detected
using CNN and LSTM. GAN’s are used for better quality deepfake videos. For the generation process, the
encoder of the original image is used but for swapping faces with the target image, the decoder of the target
image is used. They tried various techniques over Deepfake videos for devising accurate detection systems and
came to the final conclusion that the best accuracy was found when the video was split into 80 frames per
second along with a combination of CNN and LSTM. The maximum accuracy which was acquired was around
97.1%. But the accuracy which was acquired was on a set of high-resolution images. The above paper illustrates
in great detail how the Deepfake videos are generated.
Mousa Tayseer Jafar, et. al. [12] have developed a system that mainly focuses on detecting Deepfake videos
by focusing on mouth movements. The datasets used are Deepfake forensics and VID-TIMID which contains
both real and deepfake videos. The algorithm used for Deepfake detection is CNN. For detection of faces from
video frames, in the pre-processing stage, a Dlib classifier is used which will be used to detect face landmarks.
For e.g. the face according to Dlib has coordinates (49,68). In this way, the coordinates of eyebrows, nose, etc
can be known. The succeeding step excludes all frames that contain a closed mouth by calculating distances
between lips. The model proposed works on the number of words spoken per sentence. Another thing that they
have focused on is the speech rate. Generally, the speech rate is 120-150 words per minute. The system
proposed in the system uses facial expressions and speech rate to determine the Deepfake videos. It shows that
most of the Deepfake detection systems have found success in the classification of deepfake videos using CNN
and LSTM.
Pranjal Ranjan, et. al. [13] have proposed a system is based on CNN LSTM combination for classifying the
videos as fake or original. The datasets used are Faceforensics++, Celeb-DF, DeepFakeDetectionChallenge. The
best performer on the custom test set from the single dataset train models is the DFD model with an accuracy of
70.57%, while the combined train split model achieves 73.20%. The worst performing cross-test pair is of
Celeb-DF model tested on DFD. This makes sense since both datasets are opposite of each other in terms of
manipulation and visual quality. The highest cross-test accuracy is 66.23% for the model trained on DFD and
tested on DFDC. The combined test set performance is highest for the DFD model (74.03%) among the single
distribution-based model, and the combined train model records the highest classification accuracy of 86.49%.
This suggests that the Xception Net has a vast learning capacity since it can learn the various manipulations
present in all the three distributions, and is still not overfitting, conveyed by its effective, combined test
accuracy. The authors have used transfer learning in their system to increase the accuracy of thesystem.
Mingzhu Luo, et. al. [14] have proposed a system that can detect the faces present in the frame. The problem
with the CNN algorithm is that the image is converted into a fixed size and given as input, but this causes image
information to be lost and hampers the overall accuracy of the system. The authors have introduced spatial
pyramid pooling between the convolution layer and fully connected layer. By this, the problem of information
loss is subdued. The datasets used in the proposed system are CelebA, AFW, and FDDB. The image is divided
into 11*11 grid first and each grid has two prediction frames. Each prediction box has a respective probability,
confidence, and frame coordinates. The discrete accuracy of the proposed system is 95% and for continuous, it
is 74%. The above system has given perspicacity into how the accuracy of CNN algorithm can drop because of
certain factors. The system used also provided the solution that can be used so that the accuracy of CNN phase
does not drop. Also, this system has improved loss function which has enabled them to get maximum accuracy
out of the system.
Jiangang Yu, et. al. [15] have proposed a system that focuses on one drawback of the super resolution
algorithm, which is low accuracy for videos with facial changes. For super resolution, first the video is broken
down into multiple frames and then CNN is applied over each frame differently. Authors found out that when
the video has facial expression changes, it is very difficult to produce higher accuracies for super resolution
systems. To overcome this problem, the system proposed how handling of a facial image in a non-rigid manner
can be done. The system proposed in the paper works in three steps 1) global tracking 2) local alignment 3)
Super Resolution algorithm. For performance measurement of the system, authors recorded 10 video sequences
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of 10 people lasting for about 3 minutes. The PSNR value was found to be 26.6261 which is a betterment from
previous PSNR value 20.6261 using a global only approach.The above system has given insights on the
problems faced by super resolution algorithm when there are facial changes. If the facial expression changes
hamper the accuracy of super resolution, it will affect the system very badly because the output of the super
resolution phase is given as input to CNN stage in our system. To overcome this problem, the paper has given a
solution of using the handling of facial image in a non-rigid way. The PSNR value is also increased using this
approach.
Yuqian Zhou, et. al. [16] have presented a survey paper. The face detection systems do not yield satisfactory
results when the subject has poor illumination, extreme poses and when the input is having low resolution. Most
of the systems are trained only on high resolution images therefore they perform badly when they are used in
surveillance systems because of low resolution. The authors have used HoG-SVM and R-CNN and S3FD
algorithms on low resolution images. The dataset used is FDDB. The FDDB dataset has a total 5171 faces in
total 2845 images. Algorithms were tasted on performance degradation of the above models while changing the
blur, noise, or contrast level. The conclusion was both the algorithms HoG-SVM and R-CNN-S3FD perform
very badly when they are tested on low resolution images. This paper provides insights that care must be taken
as R-CNN and S3FD performs very badly for face detection of low-resolution images. Also care must be taken
for noise and contrast level as well because these factors also affect the accuracy of the algorithms.
Andreas Rossler , et. al. [17] have stated that it is difficult to detect Deepfake, either automatically or by
humans in this paper. Including a human baseline, this paper also provides the benchmark for facial
manipulation detection under random compression. The paper uses the CNN model to detect all this Deepfakes.
They cast the forgery detection as a per-frame binary classification problem of the manipulated videos. They
used total of 7 methods to detect the deepfake of the various quality of videos. In the Steganalysis method it uses
the handcrafted feature and the SVM classifier. They provided a 128 × 128 central crop-out of the face as input
to the method. It was observed that detection of raw images was good but when it came to compression factor its
accuracy decreased. A constrained convolutional layer followed by two convolutional, two max-pooling and
three fully-connected layers is used. Also a different CNN architecture is adopted with a global pooling layer
that computes four statistics (mean, variance, maximum and minimum). XceptionNet gave best output among
the other methods for the low quality video detection. The results demonstrated that the highest accuracy was
81% for the low quality images that was for XceptionNet algorithm.
Falko Matern, et. al. [18] have proposed the simple logistic regression model for detection. This paper shows
how exactly the manipulation takes place in the generated faces and the Deep fake. They proposed the algorithm
to detect completely generated faces. They demonstrated this by several visual features that focus on the eyes,
teeth, facial contours. Specular reflection in faces is most prominent in the eyes. According to them, samples
generated by Deepfake techniques show unconvincing specular reflections. They state that reflections in the
eyes are either missing or appear simplified as a white blob. For the experiment purpose, they used these 3
datasets CelebA, ProGAN, Glow . To extract color features of each eye, they use commonly available computer
vision methods. For the deepfake detection, they exploit missing reflections, and missing details in the eye and
teeth areas. Then again detect facial landmarks and crop the input image to the facial region. They have used the
neural network which is fully connected, which contains three layers with 64 nodes and ReLU activation
functions. Two classifiers such as MLP and Log Reg are used. It concludes that classification done using only
the features generated from teeth performs relatively poorly, with an AUC of 0.625 for both classifiers. Much
better performances of 0.820 and 0.784 were observed when the features were extracted from the eye region.
With the help of the combined feature vector, AUC of 0.851 is achieved by the neural network. This further
concludes that the eye and teeth features are more accurate.
Scott McCloskey, et. al. [19] have proposed the model that uses the saturation cues to detect the deep fakes.
With the help of saturation cues images can be distinguished as GAN-generated imagery or camera imagery.
Two types of GAN-generated imagery can be exposed with the potency of this que. It is seen that HDR, camera
images generally have regions of saturation under-exposure. For this forensic, the hypothesis stated is that the
frequency of saturated and under-exposed pixels will be suppressed by the generator’s normalization steps. They
suggested the use of a GAN image detector, where one can simply measure the frequency of saturated and
under-exposed pixels in each image for this purpose. Trained using Matlab’s fitcsvm function, these features are
classified by a linear Support Vector Machine (SVM). They used this method on 2 different datasets GAN Crop
and GAN Full. This method clearly does a far better job of detecting fully GAN-generated images, where it
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produces a 0.7 AUC. This paper provides an alternative for the features that can be considered while detecting
the Deepfake videos.
Atra Akandeh, et. al. [20] have proposed two variations of LSTM networks. The first variant is called LSTM
6 which has three fixed constant gates and the second variant known as LSTM C6 which is an improvement
over LSTM 6 having reduced memory-cell input block. When hyperparameters like learning rate, gate
constants, number of hidden units are set properly, then reduced parameter LSTM 6 and LSTM C6 variants can
perform as good as the standard LSTM networks. Also slim architectures enable training speedup. LSTM needs
to be constantly updated so this alternative. LSTM RNN incorporates a memory cell (vector) and includes three
gates: (i) an input gate, it (ii) an output gate ot, and (iii) a forget gate, ft. Then the number of (adaptive)
parameters in LSTM 6 is n(m + n + 1) and for LSTM C6 the total number of (adaptive) parameters is n(m + 2).
The standard LSTM (denoted as lstm0 in the figure) displays smooth profiles with (testing) accuracy around
88%. However, LSTM 6 (denoted as LSTM6 in the figure) shows fluctuations and also does not catch up with
standard LSTM. Based on the hyper parameters the accuracy of both the models fluctuates. This paper gives
insight about the alternative of the LSTM model.
Chao Dong, et. al. [21] have proposed a deep learning method for single image super resolution. For
upscaling a single low resolution image, bicubic interpolation technique is used. They have not performed any
other techniques for pre-processing. Various datasets like Set5, Set14, and BSD200 are used which contain 5, 14
and 200 images respectively. Four evaluation matrices, namely IFC, noise quality measure (NQM), weighted
peak signal-to-noise ratio (WPSNR), and multi-scale structure similarity index (MSSSIM), are used by them
other than OSNR and SSIM to check the accuracy. From all the various methods the SRCNN shows the highest
accuracy for all the various indices based on 2,3 and 4 upscaling factors. From this, it can be inferred that using
the CNN based model for the super resolution of the low quality video will give better results than any other
model.
Faten F Kharbat, et. al. [22] have proposed a method to detect Deepfake videos using Support Vector
Machine (SVM) regression. Their method uses feature points extracted from the video to train Artificial
Intelligence (AI) classifiers to detect false videos. HOG, ORB, BRISK, KAZE, SURF, and FAST are the
different feature point extraction algorithms that they have determined. This paper proposes a system that
exploits this inconsistency by extracting feature points using traditional edge feature detectors. The dataset
contains 98 videos, half of which are fake videos and the other half are real videos. All the videos are in the
format of mp4 and have approximately 30 seconds of duration. 95% accuracy has been achieved using the HOG
feature point extraction algorithm. The above-proposed system helps to find an alternative for feature detection.
Feature Detection being the most important part of the project this paper suggests that the above stated
conventional algorithm can also be used for the process of feature detection.
Chuan-Chuan Low, et. al. [23] have proposed the two experimental setups. First about the face detection
performance and detection rate, with different skin color regions and processing sequence approach, and second
about location. It proposes a framework for detecting multiple faces by using depth and skin color for digital
advertising. It uses the Viola-Jones algorithm for face detection and uses the skin color to verify the human skin
face. They have used two types of processing approaches for face and skin detection. In pre-filtering after the
skin color filtering process, the Viola-Jones algorithm is applied to the image frame to detect the presence of the
human skin face. Whereas the Post-processing approach applies the skin color analysis on the detected face
image. After that, two skin color space is applied with the Viola-Jones algorithm for the true detection rate
comparison i.e YCbCr with HSV and RGB-H-CbCr.The skin face information is processed to the next phase if
the depth information for the detected face is within the region of interest (ROI). The experiment was
implemented in Visual Studio 2017 on Intel I5 processor with 3.30GHz and 16GB RAM. It uses the david
dataset and one more unknown dataset. The results show that RGB-H-CbCr achieves 88% true detection rate
and low false-positive rate compared with the YCbCr color space under the post-processing category for
multiple persons in the shortest processing time. This paper helps to understand how multiple face detection is
carried out using the above mentioned algorithm.
Badhrinarayan Malolan, et. al. [24] have proposed a framework to detect these Deepfake videos using a Deep
Learning Approach. They have trained a Convolutional Neural Network architecture on a database of extracted
faces from FaceForensics Dataset. Besides, they have also tested the model on various Explainable AI
techniques such as Layer-Wise Relevance Propagation (LRP) and Local Interpretable Model-Agnostic
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Explanations (LIME) to provide crisp visualizations of the salient regions of the image focused on by the model.
They used the FaceForensics++ dataset which consists of 363 source actor videos as the real counterpart and
3068 manipulated videos as the fake counterpart. They have used the Xception network, which is a traditional
CNN with Depth-wise Separable Convolutions and LIME to interpret the predictions of the classifier. They
have trained their model on datasets of images with two different scales of background namely 1.3x and 2x with
the faces occupying roughly 80 to 85% and 60 to 65% area respectively and with 90.17% accuracy. First, the
extraction of the face was done using the DLIB face extractor from the frames. Then the CNN XceptionNet was
applied to extract the features. As this paper uses the same dataset of FaceForensics++ it suggests that by using
the XceptionNet algorithm of CNN one can get accurate results.
III. ANALYSIS TABLE
The table 1 summarizes the research papers on the Deepfake detection as well as on Super resolution
and it states the different techniques used for the Deepfake detection.
Table 1:Analysis Table
Sr. No Title of Paper Techniques used Dataset used Accuracy
1. Deepfake: A Survey on Facial
Forgery Technique Using
Generative Adversarial
Network[4]
1. Convolutional
Neural Network
(CNN)
2. Long Short-Term
Memory (LSTM)
Face2Face, Reddit
user deepfakes
95%
2. Deepfake Video Detection
through Optical Flow based
CNN[5]
1.Convolutional
Neural Network
(CNN)
Face2Face VGG16 81.61%,
ResNet50 75.46%
3. FSSPOTTER: Spotting 1.Convolutional FaceForensics++, 77.6%
Face-Swapped video by Spatial Neural Network Deepfake TIMIT,
and Temporal Clues [6] (CNN) UADFV, Celeb-DF
2.Long Short-Term
Memory (LSTM)
4. Generalized Zero and Few-Shot
Transfer for Facial Forgery
Detection[8]
ImageNet Large
Scale Visual
Recognition
Challenge
(ILSVRC)
2012-pretrained
ResNet-18
Faceforensics++,
Dessa, Celeb DF,
Google DFD
92.23%
5. Detail-revealing Deep Video
Super-resolution[9]
1.FlowNet-S CNN
With a sub-pixel
motion
975 sequences from
high-quality 1080p
HD video clips
Method(F3)
36.71/0.96,
Method (F5)
compensation layer 36.62/0.96
(SPMC) layer
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6. Deepfake Video Detection
Using Recurrent Neural
Network[11]
1. Convolution
Neural Networks
(CNN)
2. Long Short-Term
Memory (LSTM)
1.HOHA dataset Conv-LSTM(20
frames)
96.7%,Conv-LST
M(40 frames)
97.1%
7. Improved Generalizability of
Deep-Fakes Detection Using
Transfer Learning Based
CNN Framework[13]
1. Convolution
Neural Networks
(CNN)
2. Long Short-Term
Memory (LSTM)
1.FaceForensics++
2. Celeb-DF
3.DeepFake
Detection Challenge
With Transfer
Learning
84%,Without
Transfer Learning
75%
8. Multi-scale face detection based
on convolutional neural
network.[14]
1.Convolution
Neural Networks
(CNN)
1. CelebA
2. AFW
3. FDDB
Discrete- 95%
and for
continuous, it is
74%
9. FaceForensics++: Learning to
Detect Manipulated Facial
Images[17]
1.Xception net
(CNN)
2.LSTM
FaceForensics++ 81%
10. Exploiting Visual Artifacts to
Expose Deep Fakes and Face
Manipulations[18]
The neural network
classifier as MLP
and the logistic
regression model as
LogReg
1.CelebA
2.ProGAN , 3.Glow
MLP 84%(Eyes),
LogReg
83%(Eyes)
11. Detecting gan-generated
imagery using saturation
cues[19]
SVM classifier Image net dataset 92%
12. Image Feature Detectors for
Deep Fake Video Detection[22]
1.SVM classifier
2.Feature extractor
algorithms
Unnamed with 98
videos
HOG 94.5%,
SURF 90%,
KAZE 76.5%
13. Experimental Study on Multiple
Face Detection with Depth and
SkinColour[23]
1.Voila jones face
detection algorithm
Unnamed 88%
14. Explainable Deep-Fake
Detection Using Visual
Interpretability Methods[24]
1. Xception
net(CNN)
2. LRP and LIME
FaceForensics++ 90.17%
9. VIVA-Tech International Journal for Research and Innovation
ISSN(Online): 2581-7280
Volume 1, Issue 4 (2021)
Article No. X
PP XX-XX
VIVA Institute of Technology
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The various algorithms and features used for the Deepfake detection are analyzed in the above table. It
includes the Machine Learning and Deep Learning based techniques. From the analysis table above it can be
seen that CNN algon with the LSTM gives better results and accuracy which can be further increased by using
the Concept of Super resolution.
IV. CONCLUSION
With the increase in the use of Deepfake videos around the world, it is very much necessary to detect such
videos before they could cause some sort of harm. Various Machine Learning and Deep Learning-based
techniques along with the different features are used to classify the videos as fake or real. Among all the
different techniques used, the one that uses CNN and LSTM has proved to be more accurate in the classification
of the videos. Here, various datasets that contain several real and fake videos have been used for the
classification. From studying papers it is apparent that CNN along with LSTM yields better results and
accuracy.
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ISSN(Online): 2581-7280
Volume 1, Issue 4 (2021)
Article No. X
PP XX-XX
VIVA Institute of Technology
9th
National Conference onRole of Engineers in Nation Building – 2021 (NCRENB-2021)
10
www.viva-technology.org/New/IJRI
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