Overview Of Video Object Tracking SystemEditor IJMTER
The goal of video object tracking system is segmenting a region of interest from a video
scene and keeping track of its motion, positioning and occlusion. There are the three steps of video
object tracking system those are object detection, object classification and object tracking. Object
detection is performed to check existence of objects in video. Then the detected object can be
classified in various categories on the basis on their shape, motion, color and texture. Object tracking
is performed using monitoring object changes. This paper we are going to take overview of different
object detection, object classification and object tracking techniques and also the comparison of
different techniques used for various stages of tracking.
Overview Of Video Object Tracking SystemEditor IJMTER
The goal of video object tracking system is segmenting a region of interest from a video
scene and keeping track of its motion, positioning and occlusion. There are the three steps of video
object tracking system those are object detection, object classification and object tracking. Object
detection is performed to check existence of objects in video. Then the detected object can be
classified in various categories on the basis on their shape, motion, color and texture. Object tracking
is performed using monitoring object changes. This paper we are going to take overview of different
object detection, object classification and object tracking techniques and also the comparison of
different techniques used for various stages of tracking.
A binarization technique for extraction of devanagari text from camera based ...sipij
This paper presents a binarization method for camera based natural scene (NS) images based on edge
analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried
out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard
deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes.
The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are
compared with other standard techniques. The method is fast and works well for camera based natural
scene images.
A Comparison of People Counting Techniques viaVideo Scene AnalysisPoo Kuan Hoong
Real-time human detection and tracking from video surveillance footages is one of the most active research areas in computer vision and pattern recognition. This is due to the widespread application from being able to do it well. One such application is the counting of people, or density estimation, where the two key components are human detection and tracking. Traditional methods such as the usage of sensors are not suitable as they are not easily integrated with current video surveillance systems. As video surveillance systems are currently prevalent in most places, using vision based people counting techniques will be the logical approach. In this paper, we compared the two commonly used techniques which are Cascade Classifier and Histograms of Gradients (HOG) for human detection. We evaluated and compared these two techniques with three different video datasets with three different setting characteristics. From our experiment results, both Cascade Classifier and HOG techniques can be used for people counting to achieve moderate accuracy results.
Detection and Tracking of Moving Object: A SurveyIJERA Editor
Object tracking is the process of locating moving object or multiple objects in sequence of frames. Object
tracking is basically a challenging problem. Difficulties in tracking of an object may arise due to abrupt changes
in environment, motion of object, noise etc. To overcome such problems different tracking algorithms have been
proposed. This paper presents various techniques related to object detection and tracking..The goal of this paper
is to present a survey of these techniques.
AN ENHANCED EDGE ADAPTIVE STEGANOGRAPHY APPROACH USING THRESHOLD VALUE FOR RE...ijcsa
This paper attempts to improve the quality and the modification rate of a Stego Image. The input image
provided for estimating the quality of an image and the modified rate is a bitmap image. The threshold
value is used as a parameter for selecting the high frequency pixels from the Cover Image. The data
embedding process are performed on the pixels that are found with the help of Threshold value by using
LSBMR. The quality of an image is estimated by the value of PSNR and the modification rate of an image is
estimated by the value of MSE. The proposed approach achieves about 0.2 to 0.6 % of improvement in the
quality of an image and about 4 to 10 % of improvement in the modification rate of an image compared to
the edge detection techniques such as Sobel and Canny.
Operation-wise Attention Network for Tampering Localization Fusion.Weverify
In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in parallel, weighted by an attention mechanism to enable the selection of proper operations depending on the input signals. This weighting process can be very beneficial for cases where the input signal is very diverse, as in our case where the output signals of multiple image forensics algorithms are combined. Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/person-re-identification-and-tracking-at-the-edge-challenges-and-techniques-a-presentation-from-the-university-of-auckland/
Morteza Biglari-Abhari, Senior Lecturer at the University of Auckland, presents the “Person Re-Identification and Tracking at the Edge: Challenges and Techniques” tutorial at the May 2021 Embedded Vision Summit.
Numerous video analytics applications require understanding how people are moving through a space, including the ability to recognize when the same person has moved outside of the camera’s view and then back into the camera’s view, or when a person has passed from the view of one camera to the view of another. This capability is referred to as person re-identification and tracking. It’s an essential technique for applications such as surveillance for security, health and safety monitoring in healthcare and industrial facilities, intelligent transportation systems and smart cities. It can also assist in gathering business intelligence such as monitoring customer behavior in shopping environments. Person re-identification is challenging.
In this talk, Biglari-Abhari discusses the key challenges and current approaches for person re-identification and tracking, as well as his initial work on multi-camera systems and techniques to improve accuracy, especially fusing appearance and spatio-temporal models. He also briefly discusses privacy-preserving techniques, which are critical for some applications, as well as challenges for real-time processing at the edge.
Real Time Myanmar Traffic Sign Recognition System using HOG and SVMijtsrd
Traffic sign recognition is one of the most important research topics for enabling autonomous vehicle driving systems. In order to be deployed in driving environments, intelligent transport system must be able to recognize and respond to exceptional road conditions such as traffic signs, highway work zones and imminent road works automatically. In this paper, Real time Myanmar Traffic Sign Recognition System RMTSRS is proposed. The incoming video stream is fed into computer vision. Then each incoming frames are segmented using color threshold method for traffic sign detection. A Histogram of Oriented Gradients HOG technique is used to extract the features from the segmented traffic sign and then RMTSRS classifies traffic sign types using Support Vector Machine SVM . The system achieves classification accuracy up to 98 . Myint Tun | Thida Lwin "Real-Time Myanmar Traffic Sign Recognition System using HOG and SVM" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27929.pdfPaper URL: https://www.ijtsrd.com/computer-science/real-time-computing/27929/real-time-myanmar-traffic-sign-recognition-system-using-hog-and-svm/myint-tun
An Image Based PCB Fault Detection and Its Classificationrahulmonikasharma
The field of electronics is skyrocketing like never before. The habitat for the electronic components is a printed circuit board (PCB). With the advent of newer and finer technologies it has almost become impossible to detect the faults in a printed circuit board manually which consumes lot of manpower and time. This paper proposes a simple and cost effective method of fault diagnosis in a PCB using image processing techniques. In addition to fault detection and its classification this paper addresses various problems faced during the pre-processing phase. This paper overcomes the drawbacks of the previous works such as improper orientations of the image and size variations of the image. Basically image subtraction algorithm is used for fault detection. The most commonly occurring faults are concentrated in this work and the same are implemented using MATLAB tool.
A Probabilistic U-Net for Segmentation of Ambiguous ImagesSeunghyun Hwang
Review : A Probabilistic U-Net for Segmentation of Ambiguous Images
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Edge detection is still difficult task in the image processing field. In this paper we implemented fuzzy techniques for detecting edges in the image. This algorithm also works for medical images. In this paper we also explained about Fuzzy inference system, which is more robust to contrast and lighting variations.
A binarization technique for extraction of devanagari text from camera based ...sipij
This paper presents a binarization method for camera based natural scene (NS) images based on edge
analysis and morphological dilation. Image is converted to grey scale image and edge detection is carried
out using canny edge detection. The edge image is dilated using morphological dilation and analyzed to
remove edges corresponding to non-text regions. The image is binarized using mean and standard
deviation of edge pixels. Post processing of resulting images is done to fill gaps and to smooth text strokes.
The algorithm is tested on a variety of NS images captured using a digital camera under variable
resolutions, lightening conditions having text of different fonts, styles and backgrounds. The results are
compared with other standard techniques. The method is fast and works well for camera based natural
scene images.
A Comparison of People Counting Techniques viaVideo Scene AnalysisPoo Kuan Hoong
Real-time human detection and tracking from video surveillance footages is one of the most active research areas in computer vision and pattern recognition. This is due to the widespread application from being able to do it well. One such application is the counting of people, or density estimation, where the two key components are human detection and tracking. Traditional methods such as the usage of sensors are not suitable as they are not easily integrated with current video surveillance systems. As video surveillance systems are currently prevalent in most places, using vision based people counting techniques will be the logical approach. In this paper, we compared the two commonly used techniques which are Cascade Classifier and Histograms of Gradients (HOG) for human detection. We evaluated and compared these two techniques with three different video datasets with three different setting characteristics. From our experiment results, both Cascade Classifier and HOG techniques can be used for people counting to achieve moderate accuracy results.
Detection and Tracking of Moving Object: A SurveyIJERA Editor
Object tracking is the process of locating moving object or multiple objects in sequence of frames. Object
tracking is basically a challenging problem. Difficulties in tracking of an object may arise due to abrupt changes
in environment, motion of object, noise etc. To overcome such problems different tracking algorithms have been
proposed. This paper presents various techniques related to object detection and tracking..The goal of this paper
is to present a survey of these techniques.
AN ENHANCED EDGE ADAPTIVE STEGANOGRAPHY APPROACH USING THRESHOLD VALUE FOR RE...ijcsa
This paper attempts to improve the quality and the modification rate of a Stego Image. The input image
provided for estimating the quality of an image and the modified rate is a bitmap image. The threshold
value is used as a parameter for selecting the high frequency pixels from the Cover Image. The data
embedding process are performed on the pixels that are found with the help of Threshold value by using
LSBMR. The quality of an image is estimated by the value of PSNR and the modification rate of an image is
estimated by the value of MSE. The proposed approach achieves about 0.2 to 0.6 % of improvement in the
quality of an image and about 4 to 10 % of improvement in the modification rate of an image compared to
the edge detection techniques such as Sobel and Canny.
Operation-wise Attention Network for Tampering Localization Fusion.Weverify
In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in parallel, weighted by an attention mechanism to enable the selection of proper operations depending on the input signals. This weighting process can be very beneficial for cases where the input signal is very diverse, as in our case where the output signals of multiple image forensics algorithms are combined. Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/person-re-identification-and-tracking-at-the-edge-challenges-and-techniques-a-presentation-from-the-university-of-auckland/
Morteza Biglari-Abhari, Senior Lecturer at the University of Auckland, presents the “Person Re-Identification and Tracking at the Edge: Challenges and Techniques” tutorial at the May 2021 Embedded Vision Summit.
Numerous video analytics applications require understanding how people are moving through a space, including the ability to recognize when the same person has moved outside of the camera’s view and then back into the camera’s view, or when a person has passed from the view of one camera to the view of another. This capability is referred to as person re-identification and tracking. It’s an essential technique for applications such as surveillance for security, health and safety monitoring in healthcare and industrial facilities, intelligent transportation systems and smart cities. It can also assist in gathering business intelligence such as monitoring customer behavior in shopping environments. Person re-identification is challenging.
In this talk, Biglari-Abhari discusses the key challenges and current approaches for person re-identification and tracking, as well as his initial work on multi-camera systems and techniques to improve accuracy, especially fusing appearance and spatio-temporal models. He also briefly discusses privacy-preserving techniques, which are critical for some applications, as well as challenges for real-time processing at the edge.
Real Time Myanmar Traffic Sign Recognition System using HOG and SVMijtsrd
Traffic sign recognition is one of the most important research topics for enabling autonomous vehicle driving systems. In order to be deployed in driving environments, intelligent transport system must be able to recognize and respond to exceptional road conditions such as traffic signs, highway work zones and imminent road works automatically. In this paper, Real time Myanmar Traffic Sign Recognition System RMTSRS is proposed. The incoming video stream is fed into computer vision. Then each incoming frames are segmented using color threshold method for traffic sign detection. A Histogram of Oriented Gradients HOG technique is used to extract the features from the segmented traffic sign and then RMTSRS classifies traffic sign types using Support Vector Machine SVM . The system achieves classification accuracy up to 98 . Myint Tun | Thida Lwin "Real-Time Myanmar Traffic Sign Recognition System using HOG and SVM" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27929.pdfPaper URL: https://www.ijtsrd.com/computer-science/real-time-computing/27929/real-time-myanmar-traffic-sign-recognition-system-using-hog-and-svm/myint-tun
An Image Based PCB Fault Detection and Its Classificationrahulmonikasharma
The field of electronics is skyrocketing like never before. The habitat for the electronic components is a printed circuit board (PCB). With the advent of newer and finer technologies it has almost become impossible to detect the faults in a printed circuit board manually which consumes lot of manpower and time. This paper proposes a simple and cost effective method of fault diagnosis in a PCB using image processing techniques. In addition to fault detection and its classification this paper addresses various problems faced during the pre-processing phase. This paper overcomes the drawbacks of the previous works such as improper orientations of the image and size variations of the image. Basically image subtraction algorithm is used for fault detection. The most commonly occurring faults are concentrated in this work and the same are implemented using MATLAB tool.
A Probabilistic U-Net for Segmentation of Ambiguous ImagesSeunghyun Hwang
Review : A Probabilistic U-Net for Segmentation of Ambiguous Images
- by Seunghyun Hwang (Yonsei University, Severance Hospital, Center for Clinical Data Science)
Edge detection is still difficult task in the image processing field. In this paper we implemented fuzzy techniques for detecting edges in the image. This algorithm also works for medical images. In this paper we also explained about Fuzzy inference system, which is more robust to contrast and lighting variations.
Design and implementation of video tracking system based on camera field of viewsipij
The basic idea of this paper is to design and implement of video tracking system based on Camera Field of
View (CFOV), Otsu’s method was used to detect targets such as vehicles and people. Whereas most
algorithms were spent a lot of time to execute the process, an algorithm was developed to achieve it in a
little time. The histogram projection was used in both directional to detect target from search region,
which is robust to various light conditions in Charge Couple Device (CCD) camera images and saves
computation time.
Our algorithm based on background subtraction, and normalize cross correlation operation from a series
of sequential sub images can estimate the motion vector. Camera field of view (CFOV) was determined and
calibrated to find the relation between real distance and image distance. The system was tested by
measuring the real position of object in the laboratory and compares it with the result of computed one. So
these results are promising to develop the system in future.
Target Detection Using Multi Resolution Analysis for Camouflaged Images ijcisjournal
Target detection is a challenging problem having many applications in defense and civil. Most of the
targets in defense are camouflaged. It is difficult for a system to detect camouflaged targets in an image. A
novel and constructive approach is proposing to detect object in camouflage images. This method uses
various methodologies such as 2-D DWT, gray level co-occurrence matrix (GLCM), wavelet coefficient
features, region growing algorithm and canny edge detection. Target detection is achieved by calculating
wavelet coefficient features from GLCM of transformed sub blocks of the image. Seed block is obtained by
evaluating wavelet coefficient features. Finally the camouflage object is highlighted using image
processing schemes. The proposed target detection system is implemented in Matlab 7.7.0 and tested on
different kinds of images.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Passive Image Forensic Method to Detect Resampling Forgery in Digital Imagesiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Abstract: Image processing refers to a type of signal processing where the input is an image and output is an image or some of the characteristics of the image such as objects in image, contrast and many more. Edge Detection is considered as one of the most important process in the field of image processing. The existing edge detection algorithms like sobel, prewitt, canny, etc have various limitations. These limitations are overcome using a technique like fuzzy logic. This paper discusses about use of fuzzy logic for edge detection along with some other edge detection techniques incorporated as input the fuzzy system and provides an algorithm for the same.. The paper provides a comparison of the algorithm with varied inputs for real image.
AN ENHANCED BLOCK BASED EDGE DETECTION TECHNIQUE USING HYSTERESIS THRESHOLDING sipij
Edge detection is a crucial step in various image processing systems like computer vision , pattern
recognition and feature extraction. The Canny edge detection algorithm even though exhibits high
accuracy, is computationally more complex compared to other edge detection techniques. A block based
distributed edge detection technique is presented in this paper, which adaptively finds the thresholds for
edge detection depending on block type and the distribution of gradients in each block. A novel method of
computation of high threshold has been proposed in this paper. Block-based hysteresis thresholds are
computed using a non uniform gradient magnitude histogram. The algorithm exhibits remarkably high
edge detection accuracy, scalability and significantly reduced computational time. Pratt’s Figure of Merit
quantifies the accuracy of the edge detector, which showed better values than that of original Canny and
distributed Canny edge detector for benchmark dataset. The method detected all visually prominent edges
for diverse block size.
Similar to Knowledge-based Fusion for Image Tampering Localization (20)
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.
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 Detection: The Importance of Training Data Preprocessing and Practic...Symeon Papadopoulos
Talk on the AI4Media Workshop on GANs for Media Content Generation, October 1st 2020, https://ai4media.eu/events/gan-media-generation-workshop-oct-2020/
Short panel presentation given in the context of the AI4EU WebCafe "The COVID-19 and Contact Tracing Apps" on June 23rd 2020, focusing on the problem of COVID-19 misinformation and how this could potentially affect the adoption of contact tracing apps.
Lecture given on January 28, 2019 to post-graduate students of the Computer Engineering and Media program, at the School of Journalism and Media, Aristotle University of Thessaloniki.
Presentation on the topic of sensing air-quality at city level based on Twitter data given at the IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) 2018 workshop in Aristi, Greece.
Aggregating and Analyzing the Context of Social Media ContentSymeon Papadopoulos
Introduction to the Context Analysis and Aggregation service of InVID. Given at the Workshop on Content Verification Tools hosted by the journalists' association in Thessaloniki, Greece on June 6, 2018.
Summary of problems and research results on the problem of verifying multimedia content on the Internet. Includes results from the REVEAL and InVID research projects. Presented at the Technology Forum, Thessaloniki, May 16, 2018.
Presentation of web-based service developed within REVEAL and InVID on Experts’ Meeting on Digital Image Authentication and Classification, December 6, 2017.
Tutorial for ACM Multimedia 2016, given together with Gerald Friedland, with contributions from Julia Bernd and Yiannis Kompatsiaris. The presentation covered an introduction to the problem of disclosing personal information through multimedia sharing, the associated security risks, methods for conducting multimodla inferences and technical frameworks that could help alleviate such risks.
Presentation of the joint participation between CERTH and CEA LIST in the MediaEval 2015 edition of the Retrieving Diverse Social Images Task in Wurzen, Germany on 14-15 September, 2015.
Presentation of the joint participation between CERTH and CEA LIST in the 2015 edition of the MediaEval Placing Task in Wurzen, Germany, September 14-15, 2015.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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Generative AI Deep Dive: Advancing from Proof of Concept to Production
Knowledge-based Fusion for Image Tampering Localization
1. Knowledge-based fusion for
image tampering localization
Chyssanthi Iakovidou, Symeon Papadopoulos
and Yiannis Kompatsiaris
Multimedia Knowledge and Social Media Analytics Lab (MKLab, https://mklab.iti.gr/)
Information Technologies Institute (ITI), Centre for Research and Technology Hellas (CERTH)
June 5-7, 2020 @ AIAI 2020
2. Background
1 Image forensics
Image forensics deals with the problem of detecting
tampered images at:
• Image-level (Tampering detection)
two-class classification problem with the goal to
classify given image as authentic/forged
• Pixel-level (Tampering localization)
reporting and localizing tampered image regions
through heat-maps where tampered and non-
tampered pixels have distinct values
Tampering detection report;
Tampering localization report;
Heat-maps:
Label: “forged”
score: 70%
3. Background
2 Image forensics and current challenges
Challenges:
• Different tampering sessions leave different traces
SoA tools are designed to detect tampering based on only subsets of traces.
• In real scenarios a single forgery session includes multiple different manipulations
Analysis of different tools may be needed to get a robust forensic report
• Forged images shared over the Internet, undergo further transformations
Cropping, resizing, resaving disrupt tampering traces and hinder tools’ efficiency
• Few realistic tampering datasets available for experimenting
Realistic tampering is not easily achieved through automatic procedures
4. Proposed approach
3 Tamperinglocalization fusion framework
Goal
• Introduce extensible fusion framework for tampering localization and output refinement
Design strategy
• Select and categorize SoA approaches on a multi-criterion ranking and grouping process
• Integrate expert background knowledge on the behaviour of SoA approaches (types of images,
encoding, supported traces, known limitations, etc.)
• Employ a fusion mechanism based on local and cross-tool statistics to produce a single, refined fused
heat-map output for tampering localization
Benefits
• Leverage tools that are complementary to each other
• Present tampering localization results to end users in a manner that is easier to interpret
5. Proposed approach
3a Candidate selection process
• Detection of JPEG compression traces, in the transform domain.
• Detection of JPEG compression traces, in the spatial domain
• Noise-based detection
6. 3a
Proposed approach
Candidate selection process
• Evaluation on both realistic and synthetic
benchmark datasets
• Evaluation extended to include reports after
various post-processing operations have been
applied on the original datasets
• The ability to retrieve true positives of tampered images
at a low level of false positives (KS@0.05);
• The ability to achieve good localization of the tampered
region within the image (F1)
• The readability of the produced heat map, i.e., a high
distinction of assigned values for tampered and
untampered pixels expressed as the range of different
binarization thresholds that result in high F1 scores.
7. 3a
Proposed approach
Candidate selection process
a) performance: KS score, max F1 score; threshold binarization range,
b) average performance of methods based on normalized KS, F1 and threshold range per dataset;
c) rank aggregation results based on Borda count,Copeland and Kemeny-Young voting
8. 3a
Proposed approach
Candidate selection process
After evaluations of 14 established state-of-the-art methods [1] for splicing localization the
following were selected:
• ADQ1 [2] and DCT [3] are based on analysis of JPEG compression in the transform domain
• BLK [4] and CAGI [5] are based on analysis of JPEG compression in the spatial domain
• NOI3 [6] is a noise-based detector and is integrated as a complementary tool.
[1] M. Zampoglou, et al., "Large-scale evaluation of splicing localization algorithms for web images," MultimTools Appl., vol. 76, no. 4, p. 4801–4834, 2016.
[2] Z. Lin et al., "Fast, automatic and fine-grained tampered {JPEG} image detection via {DCT} coefficient analysis," Pattern Recognition, vol. 42, no. 11, 2009.
[3] S. Ye, et al, "Detecting digital image forgeries by measuring inconsistencies of blocking artifact," in IEEE Int. Conference on Multimedia and Expo, 2007.
[4] W. Li, et al., "Passive detection of doctored JPEG image via block artifact grid extraction," Signal Processing, vol. 89, no. 9, p. 1821:1829, 2009.
[5] Iakovidou, et al. (2018). Content-aware detection of JPEG grid inconsistencies for intuitive image forensics. J. of Visual Com. and Image Representation, 54, 155-170..
[6] D. Cozzolino, et al, "Splicebuster: A new blind image splicing detector," in IEEE International Workshop on Information Forensics and Security, 2015.
9. Proposed approach
3b Fusion strategy
Designing an extensible fusion and output refinement framework for tampering localization
11. Proposed approach
Binarization Unit
Automate map binarization by choosing
the binarization threshold as a value
belonging to the respective safe ranges
per method (empirically defined)
12. Proposed approach
Connected Component Unit
For every 8-connected region (blob) of
the binarized map we calculate centroid.
Next, we build a feature vector:
• # detected connected regions,
• location of centroids
• spatial standard deviation of the
pixels belonging to a region from their
respective centroid,
• image area of blob (bounding box)
containing the pattern of interest.
13. Proposed approach
Filtering Unit
Normalized maps and outputs from
Component Unit used for filtering.
First, we filter based on findings of each
method independently from one another:
• Blobs discarded if too big or too small
• Blobs whose bounding boxes overlap
by more than 90% are merged
• Blobs ranked and top 5 selected
based on i) centroids distance from
the overall map centroid, ii) density of
pixels in the blob, and iii) their size.
14. Proposed approach
Filtering Unit
Perform a content-aware filtering step
utilizing information extracted by
respective methods (CAGI and DCT) to
filter blobs that may occur as false
localizations, such as:
- Over/under exposed image areas
- Image areas of intense texture
- Image areas of consistent intensity
values
15. Proposed approach
Statistics Extraction Unit
Extraction of statistics to automate the
evaluation of the outputs’ usefulness
• Multilevel measurements of the
entropy of data and the Kolmogorov-
Smirnov (KS) statistic
16. Proposed approach
Fusion Unit
Leverages the intermediate calculations
to produce a single fused output.
• Interpretability of maps: Maps
ranked and assigned a confidence
score, Ci, based on difference of the
entropy before and after binarization.
• Compatibility between the traces
detected by different methods:
Confidence of a method is reinforced
if other methods detecting similar
traces also achieve high confidence.
17. Proposed approach
Fusion Unit
• Reliability of method: Score
assigned to each method during the
candidate selection process is used
to rank the methods to help define
their contribution to the final outcome
• Confidence in the presence of
identified tampered regions: The
blobs with the highest KS score of
the best ranking method serve as
baselines. The final refined map is
constructed through comparisons of
the baseline with blob mask of other
methods in a ranked, weighted order.
18. Experimental Evaluation
4 Experimental setup
We tested on two publicly available datasets:
• The First IFS-TC Image Forensics Challenge
training set that contains 450 user-submitted
forgeries and was designed to serve as a realistic
benchmark.
• The CASIA V2.0 dataset contains 5,123
realistically tampered color images of varying sizes
It includes uncompressed images and also JPEG
images with different quality factors.
19. Experimental Evaluation
4 Experimental setup
Evaluation metrics
Overall localization quality and readability is based on the pixel-wise agreement between the
reference mask (Ground Truth, GT) and the produced tampering localization heat map and it is
measured in terms of the achieved F-score (F1).
𝐹1 = 2𝑇𝑃/(2𝑇𝑃 + 𝐹𝑃 + 𝐹𝑁)
where (TP) number of true positive, (TN) number of true negative, (FP) number of false positive, and
(FN) number of false negative.
This evaluation methodology requires the output maps to be thresholded prior to any evaluation:
• normalize all maps in the [0, 1] range,
• successively shift the binarization threshold by 0.05 increments (step), and
• calculate the achieved F1 score for every step
20. Experimental Evaluation
4 Experimental results
F1 score curves on the (a) Challenge and (b) CASIA2 datasets for FUSED and five base methods.
Localization quality
21. Experimental Evaluation
4 Experimental results
Best mean F1 score and binarization range that allows F1 to remain high (> 70% of respective
maximum F1 score) and reported detections for F1 score >= 0.7 at each method's best binarization
threshold for Challenge and CASIA2 datasets.
Unique Localizations corresponds to the number of detections exclusively achieved by that method
Output interpretability
23. Discussion
5 Experimental findings and next steps
• In both datasets the fused output achieves high F1 scores over a wide range of
thresholds → increased localization ability and interpretability.
• In both datasets the fused method reports a high number of absolute localizations
while also contributing additional unique localizations through fusion and refinement of
the available individual outputs.
• Overall, we verified the importance of exploiting the available state-of-the-art methods
in a manner that improves the robustness and user-friendliness of the output.
• Next steps include introducing more methods to the framework and eliminating hard-
coded expert knowledge in the fusion criteria and rules moving towards introducing
fusion approaches based on supervised learning.
24. Thank you
Partially funded by the European Commission under contract num. H2020-825297 WeVerify and H2020-700024 TENSOR
Dr. Symeon Papadopoulos [papadop@iti.gr] [@sympap]
Media Verification team, http://mever.iti.gr/
Multimedia Knowledge and Social Media Analytics Lab,
https://mklab.iti.gr/