Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
Classification and evaluation of digital forensic toolsTELKOMNIKA JOURNAL
Digital forensic tools (DFTs) are used to detect the authenticity of digital images. Different DFTs have been developed to detect the forgery like (i) forensic focused operating system, (ii) computer forensics, (iii) memory forensics, (iv) mobile device forensics, and (v) software forensics tools (SFTs). These tools are dedicated to detect the forged images depending on the type of the applications. Based on our review, we found that in literature of the DFTs less attention is given to the evaluation and analysis of the forensic tools. Among various DFTs, we choose SFTs because it is concerned with the detection of the forged digital images. Therefore,the purpose of this study is to classify the different DFTs and evaluate the software forensic tools (SFTs) based on the different features which are present in the SFTs. In our work, we evaluate the following five SFTs, i.e.,“FotoForensics”, “JPEGsnoop”, “Ghiro”, “Forensically”, and “Izitru”, based on different features so that new research directions can be identified for the development of the SFTs.
IMAGE QUALITY ASSESSMENT FOR FAKE BIOMETRIC DETECTION: APPLICATION TO IRIS, F...ijiert bestjournal
In this Paper,the actual presence of a real legitimate trait in contrast to a fake self - manufactured synthetic or reconstructed sample is a significant problem in biometric authentication,which requires the development of new and efficient protection measures. In this paper,we present a novel software - based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The obje ctive of the proposed system is to enhance the security of biometric recognition frameworks,by adding livens assessment in a fast,user - friendly,and non - intrusive manner,through the use of image quality assessment. The proposed approach presents a very low degree of complexity,which makes it suitable for real - time applications,using 25 general image quality features extracted from one image (i.e.,the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results,obtained on publicly available data sets of fingerprint,iris,and 2D face,show that the proposed method is highly competitive compared with other state - of - the - art approaches and that the analysis of the general image quality of rea l biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.
A revolution in computer interface design is changing the way we think about computers. Rather than typing on a keyboard and watching a television monitor, Augmented Reality lets people use familiar, everyday objects in ordinary ways. A revolution in computer interface design is changing the way we think about computers. Rather than typing on a keyboard and watching a television monitor, Augmented Reality lets people use familiar, everyday objects in ordinary ways. This paper surveys the field of Augmented Reality, in which 3-D virtual objects are integrated into a 3-D real environment in real time. It describes the medical, manufacturing, visualization, path planning, entertainment and military applications that have been explored. This paper describes the characteristics of Augmented Reality systems. Registration and sensing errors are two of the biggest problems in building effective Augmented Reality systems, so this paper throws light on problems. Future directions and areas requiring further research are discussed. This survey provides a starting point for anyone interested in researching or using Augmented Reality.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
In imaging science, the photo editing software packages can alter the original images without any
detecting traces of tampering. Hence, the image forgery detection technique plays an important role in
verifying the integrity of digital image forensics for authentication. The techniques such as
watermarking are used for authentication but it can be modified through third parties attack through
extraction. Malicious and digital imaging (digital products) tamper detection is the subject of this
article. In particular, we focus on a special type of digital forgery detection - copy attack campaign, in
which part of the image is copied and pasted into the image and the cover features a large image of
intentions another. In this paper, we investigate the dynamic forged copy detection problem, and
describes a highly efficient and reliable detection method that based on image source ANN
identification.. Even when the region is enhanced copy / retouching and background merger, and the
method can successfully identify counterfeit forgery when images are saved in a lossy format (such as
JPEG). The performance of the method's performance several forged images.
Classification and evaluation of digital forensic toolsTELKOMNIKA JOURNAL
Digital forensic tools (DFTs) are used to detect the authenticity of digital images. Different DFTs have been developed to detect the forgery like (i) forensic focused operating system, (ii) computer forensics, (iii) memory forensics, (iv) mobile device forensics, and (v) software forensics tools (SFTs). These tools are dedicated to detect the forged images depending on the type of the applications. Based on our review, we found that in literature of the DFTs less attention is given to the evaluation and analysis of the forensic tools. Among various DFTs, we choose SFTs because it is concerned with the detection of the forged digital images. Therefore,the purpose of this study is to classify the different DFTs and evaluate the software forensic tools (SFTs) based on the different features which are present in the SFTs. In our work, we evaluate the following five SFTs, i.e.,“FotoForensics”, “JPEGsnoop”, “Ghiro”, “Forensically”, and “Izitru”, based on different features so that new research directions can be identified for the development of the SFTs.
IMAGE QUALITY ASSESSMENT FOR FAKE BIOMETRIC DETECTION: APPLICATION TO IRIS, F...ijiert bestjournal
In this Paper,the actual presence of a real legitimate trait in contrast to a fake self - manufactured synthetic or reconstructed sample is a significant problem in biometric authentication,which requires the development of new and efficient protection measures. In this paper,we present a novel software - based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The obje ctive of the proposed system is to enhance the security of biometric recognition frameworks,by adding livens assessment in a fast,user - friendly,and non - intrusive manner,through the use of image quality assessment. The proposed approach presents a very low degree of complexity,which makes it suitable for real - time applications,using 25 general image quality features extracted from one image (i.e.,the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results,obtained on publicly available data sets of fingerprint,iris,and 2D face,show that the proposed method is highly competitive compared with other state - of - the - art approaches and that the analysis of the general image quality of rea l biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.
A revolution in computer interface design is changing the way we think about computers. Rather than typing on a keyboard and watching a television monitor, Augmented Reality lets people use familiar, everyday objects in ordinary ways. A revolution in computer interface design is changing the way we think about computers. Rather than typing on a keyboard and watching a television monitor, Augmented Reality lets people use familiar, everyday objects in ordinary ways. This paper surveys the field of Augmented Reality, in which 3-D virtual objects are integrated into a 3-D real environment in real time. It describes the medical, manufacturing, visualization, path planning, entertainment and military applications that have been explored. This paper describes the characteristics of Augmented Reality systems. Registration and sensing errors are two of the biggest problems in building effective Augmented Reality systems, so this paper throws light on problems. Future directions and areas requiring further research are discussed. This survey provides a starting point for anyone interested in researching or using Augmented Reality.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
In imaging science, the photo editing software packages can alter the original images without any
detecting traces of tampering. Hence, the image forgery detection technique plays an important role in
verifying the integrity of digital image forensics for authentication. The techniques such as
watermarking are used for authentication but it can be modified through third parties attack through
extraction. Malicious and digital imaging (digital products) tamper detection is the subject of this
article. In particular, we focus on a special type of digital forgery detection - copy attack campaign, in
which part of the image is copied and pasted into the image and the cover features a large image of
intentions another. In this paper, we investigate the dynamic forged copy detection problem, and
describes a highly efficient and reliable detection method that based on image source ANN
identification.. Even when the region is enhanced copy / retouching and background merger, and the
method can successfully identify counterfeit forgery when images are saved in a lossy format (such as
JPEG). The performance of the method's performance several forged images.
Reviewing the Effectivity Factor in Existing Techniques of Image Forensics IJECEIAES
Studies towards image forensics are about a decade old and various forms of research techniques have been presented till date towards image forgery detection. Majority of the existing techniques deals with identification of tampered regions using different forms of research methodologies. However, it is still an open-end question about the effectiveness of existing image forgery detection techniques as there is no reported benchmarked outcome till date about it. Therefore, the present manuscript discusses about the most frequently addressed image attacks e.g. image splicing and copy-move attack and elaborates the existing techniques presented by research community to resist it. The paper also contributes to explore the direction of present research trend with respect to tool adoption, database adoption, and technique adoption, and frequently used attack scenario. Finally, significant open research gap are explored after reviewing effectiveness of existing techniques.
Face Recognition and Increased Reality System for Mobile Devicesijtsrd
The objective of this article is to explain the problems of using the facial recognition functions in current mobile devices, as well as to give a possible solution based on a client server design. Sirojiddin Tavboev | Tavboev Islom "Face Recognition and Increased Reality System for Mobile Devices" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31384.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31384/face-recognition-and-increased-reality-system-for-mobile-devices/sirojiddin-tavboev
A REVIEW ON BLIND STILL IMAGE STEGANALYSIS TECHNIQUES USING FEATURES EXTRACTI...IJCSEIT Journal
Steganography is the technique for hiding secret information in other data such as still, multimedia
images, text, audio. Whereas Steganalysis is the reverse technique in which detection of the secret
information is done in the stego image. Steganalysis can be classified on the basis of the techniques used
classified statistical techniques, pattern classification techniques and visual detection techniques .All the
existing techniques can be broadly classified on the basis of the information required for the designing of
the steganalysis. They are targeted and blind steganalysis In targeted technique, we first look at
steganalysis techniques is designed for a particular steganographic embedding algorithm in mind whereas
in blind steganalysis is general class of steganalysis techniques which can be implemented with any
steganographic embedding algorithm, even an unknown algorithm. In this paper, an extensive review
report is presented chronologically on the Blind Image Steganalysis for the still stego images using the
classification techniques.
Fake Multi Biometric Detection using Image Quality Assessmentijsrd.com
In the recent era where technology plays a prominent role, persons can be identified (for security reasons) based on their behavioral and physiological characteristics (for example fingerprint, face, iris, key-stroke, signature, voice, etc.) through a computer system called the biometric system. In these kinds of systems the security is still a question mark because of various intruders and attacks. This problem can be solved by improving the security using some efficient algorithms available. Hence the fake person can be identified if he/she uses any synthetic sample of an authenticated person and a fake person who is trying to forge can be identified and authenticated.
Dermatological diagnosis by mobile applicationjournalBEEI
Health care mobile application delivers the right information at the right time and place to benefit patient’s clinicians and managers to make correct and accurate decisions in health care fields, safer care and less waste, errors, delays and duplicated errors.Lots of people have knowledge a skin illness at some point of their life, For the reason that skin is the body's major organ and it is quite exposed, significantly increasing its hazard of starting to be diseased or ruined.This paper aims to detect skin disease by mobile app using android platform providing valid trustworthy and useful dermatological information on over 4 skin diseases such as acne, psoriasis content for each skin condition, skin rush and Melanoma. It will include name, image, description, symptoms, treatment and prevention with support multi languages English and Bahasa and Mandarin. the application has the ability to take and send video as well as normal and magnified photos to your dermatologist as an email attachment with comments on safe secure network, this app also has a built in protected privacy features to access to your photo and video dermatologists. The mobile application help in diagnose and treat their patients without an office visit teledermatology is recognized by all major insurance companies doctor.
REVIEW ON GENERIC OBJECT RECOGNITION TECHNIQUES: CHALLENGES AND OPPORTUNITIES IAEME Publication
Recognizing objects automatically from an image is a fundamental step for many real-world computer vision applications. It is the task of identifying an instance of object in an image or video sequence without or least human intervention and assistance. In-spite of very high complexity, human beings perform this task with very less effort and even in the state of least attention. Little effort is needed for the humans to recognize huge number of and various categories of objects in images, though ‘object’ in the image may be different with respect to size / scale, viewpoint, position or orientation. We are even able to recognize the objects from an image, when they are only partially visible or present against cluttered background. Not only this, the recognition can be for specific instance of object or object category/class. When the task is done for classes of the object it is known as Generic object recognition or object-class detection or category-level object recognition. It has been found that over the years many techniques have evolved for recognizing object classes from images, but any automated object recognition system till date has not gained this capability fully at par with human beings. This very fact makes recognition of objects from an image, the most basic and fundamental challenge in the field of computer vision research. The purpose of this study is to give an overview and categorization of the approaches used in the literature for the purpose of Generic Object Recognition and various technical advancements achieved in the field. Mostly the survey focusses on the leading work since year 2000.
Medical vision: Web and mobile medical image retrieval system based on google...IJECEIAES
The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image.
Data Hiding In Medical Images by Preserving Integrity of ROI Using Semi-Rever...IJERA Editor
Text fusion in images is an important technology for image processing. We have lots of important information related to the patient’s reports and need lots of space to store and the proper position and name which relates that image with that data. In our work we are going to find out the ROI (region of interest) for the particular image and will fuse the related document in the NROI (non-region of interest) of the image, till yet we have many techniques to fuse text data in the medical images one of form them is to fuse data at the boarders of the images and build the particular and pre-defined boarder space. We have propose an algorithm in which we first find out the area of interest and after that we find noisy pixels of the image to embed data in that noisy portions of the image. We use wavelets for smoothing images and segmentation process for extracting region of interest. Coordinates of the noisy pixels have been located and data has been embedded in those pixels .The used embedding technique embed data in least significant bits, hence does not degrade the quality of the image to unacceptable limits. Results show that it gives good PSNR and MSE values which are used for measuring quality effected performance.
The main objective of this work is the uniting and streamlining of an automatic face detection application and recognition system for video indexing applications. Human identification means the classification of gender which can increase the identification accuracy. So, accurate gender classification algorithms may increase the accuracy of the applications and can reduce its complexity. But, in some applications, some challenges are there such as rotation, gray scale variations that may reduce the accuracy of the application. The main goal of building this module is to understand the values in image, pattern, and array processing with OpenCV for effective processing faces for building pipe-lining, SVM models.
Development of durian leaf disease detection on Android device IJECEIAES
Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent’s objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in this felids, the paper proposed the face recognition as sensitive learning task experiments on a large face databases demonstrate of the new feature. The researcher recommends that there's a needs to evaluate the previous studies and researches, especially on face recognition field and 3D, hopeful for advanced techniques and methods in the near future.
An SVM based Statistical Image Quality Assessment for Fake Biometric DetectionIJTET Journal
Abstract
A biometric system is a computer based system and is used to identify the person on their behavioral and logical characteristics such as (for example fingerprint, face, iris, keystroke, signature, voice, etc.).A typical biometric system consists of feature extraction and matching patterns. But nowadays biometric systems are attacked by using fake biometric samples. This paper described the fingerprint biometric techniques and also introduce the attack on that system and by using Image Quality Assessment for Liveness Detection to know how to protect the system from fake biometrics and also how the multi biometric system is more secure than uni-biometric system. Support Vector Machine (SVM) classification technique is used for training and testing the fingerprint images. The testing onput fingerprint image is resulted as real and fake fingerprint image by quality score matching with the training based real and fake fingerprint samples.
This study is a part of design of an audio system for in-house object detection system for visually impaired,
low vision personnel by birth or by an accident or due to old age. The input of the system will be scene and
output as audio. Alert facility is provided based on severity levels of the objects (snake, broke glass etc) and
also during difficulties. The study proposed techniques to provide speedy detection of objects based on
shapes and its scale. Features are extraction to have minimum spaces using dynamic scaling. From a
scene, clusters of objects are formed based on the scale and shape. Searching is performed among the
clusters initially based on the shape, scale, mean cluster value and index of object(s). The minimum
operation to detect the possible shape of the object is performed. In case the object does not have a likely
matching shape, scale etc, then the several operations required for an object detection will not perform;
instead, it will declared as a new object. In such way, this study finds a speedy way of detecting objects.
For Image Authentication Problem using Encryption Technique and LDPC Source Coding is necessary in Content
Delivery via unsecure medium, Like Peer-To-Peer (P2P) File Sharing. These transferring Digital Files from one Computer to
another. Images are the Most Important Utility of our life. They are used in many applications. There are Two Main Goals of
Image Security: Image Encryption and Authentication. More different encoded versions of the original image available.In
addition, unsecure medium might tamper with the contents.. We propose an efficient, accurate, reliable process using
encryption and LDPC source coding for the image authentication problem. The key idea is to provide a Slepian-Wolf encoded
as authentication data which is encrypted using cryptography key before ready to send. The key used for encryption is usually
independent of the Plain-Image. This can be decoded with side information of an authentic image.
Reviewing the Effectivity Factor in Existing Techniques of Image Forensics IJECEIAES
Studies towards image forensics are about a decade old and various forms of research techniques have been presented till date towards image forgery detection. Majority of the existing techniques deals with identification of tampered regions using different forms of research methodologies. However, it is still an open-end question about the effectiveness of existing image forgery detection techniques as there is no reported benchmarked outcome till date about it. Therefore, the present manuscript discusses about the most frequently addressed image attacks e.g. image splicing and copy-move attack and elaborates the existing techniques presented by research community to resist it. The paper also contributes to explore the direction of present research trend with respect to tool adoption, database adoption, and technique adoption, and frequently used attack scenario. Finally, significant open research gap are explored after reviewing effectiveness of existing techniques.
Face Recognition and Increased Reality System for Mobile Devicesijtsrd
The objective of this article is to explain the problems of using the facial recognition functions in current mobile devices, as well as to give a possible solution based on a client server design. Sirojiddin Tavboev | Tavboev Islom "Face Recognition and Increased Reality System for Mobile Devices" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31384.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31384/face-recognition-and-increased-reality-system-for-mobile-devices/sirojiddin-tavboev
A REVIEW ON BLIND STILL IMAGE STEGANALYSIS TECHNIQUES USING FEATURES EXTRACTI...IJCSEIT Journal
Steganography is the technique for hiding secret information in other data such as still, multimedia
images, text, audio. Whereas Steganalysis is the reverse technique in which detection of the secret
information is done in the stego image. Steganalysis can be classified on the basis of the techniques used
classified statistical techniques, pattern classification techniques and visual detection techniques .All the
existing techniques can be broadly classified on the basis of the information required for the designing of
the steganalysis. They are targeted and blind steganalysis In targeted technique, we first look at
steganalysis techniques is designed for a particular steganographic embedding algorithm in mind whereas
in blind steganalysis is general class of steganalysis techniques which can be implemented with any
steganographic embedding algorithm, even an unknown algorithm. In this paper, an extensive review
report is presented chronologically on the Blind Image Steganalysis for the still stego images using the
classification techniques.
Fake Multi Biometric Detection using Image Quality Assessmentijsrd.com
In the recent era where technology plays a prominent role, persons can be identified (for security reasons) based on their behavioral and physiological characteristics (for example fingerprint, face, iris, key-stroke, signature, voice, etc.) through a computer system called the biometric system. In these kinds of systems the security is still a question mark because of various intruders and attacks. This problem can be solved by improving the security using some efficient algorithms available. Hence the fake person can be identified if he/she uses any synthetic sample of an authenticated person and a fake person who is trying to forge can be identified and authenticated.
Dermatological diagnosis by mobile applicationjournalBEEI
Health care mobile application delivers the right information at the right time and place to benefit patient’s clinicians and managers to make correct and accurate decisions in health care fields, safer care and less waste, errors, delays and duplicated errors.Lots of people have knowledge a skin illness at some point of their life, For the reason that skin is the body's major organ and it is quite exposed, significantly increasing its hazard of starting to be diseased or ruined.This paper aims to detect skin disease by mobile app using android platform providing valid trustworthy and useful dermatological information on over 4 skin diseases such as acne, psoriasis content for each skin condition, skin rush and Melanoma. It will include name, image, description, symptoms, treatment and prevention with support multi languages English and Bahasa and Mandarin. the application has the ability to take and send video as well as normal and magnified photos to your dermatologist as an email attachment with comments on safe secure network, this app also has a built in protected privacy features to access to your photo and video dermatologists. The mobile application help in diagnose and treat their patients without an office visit teledermatology is recognized by all major insurance companies doctor.
REVIEW ON GENERIC OBJECT RECOGNITION TECHNIQUES: CHALLENGES AND OPPORTUNITIES IAEME Publication
Recognizing objects automatically from an image is a fundamental step for many real-world computer vision applications. It is the task of identifying an instance of object in an image or video sequence without or least human intervention and assistance. In-spite of very high complexity, human beings perform this task with very less effort and even in the state of least attention. Little effort is needed for the humans to recognize huge number of and various categories of objects in images, though ‘object’ in the image may be different with respect to size / scale, viewpoint, position or orientation. We are even able to recognize the objects from an image, when they are only partially visible or present against cluttered background. Not only this, the recognition can be for specific instance of object or object category/class. When the task is done for classes of the object it is known as Generic object recognition or object-class detection or category-level object recognition. It has been found that over the years many techniques have evolved for recognizing object classes from images, but any automated object recognition system till date has not gained this capability fully at par with human beings. This very fact makes recognition of objects from an image, the most basic and fundamental challenge in the field of computer vision research. The purpose of this study is to give an overview and categorization of the approaches used in the literature for the purpose of Generic Object Recognition and various technical advancements achieved in the field. Mostly the survey focusses on the leading work since year 2000.
Medical vision: Web and mobile medical image retrieval system based on google...IJECEIAES
The application of information technology is rapidly utilized in the medical system. There is also a massive development in the automatic method for recognizing and detecting objects in the real world. In this study, we present a system called Medical Vision which is designed for people who has no expertise in medical. Medical Vision is a web and mobile-based application to give an initial knowledge in a medical image. This system has 5 features; object detection, web detection, object labeling, safe search, and image properties. These features are run by embedding Google Vision API in the system. We evaluate this system by observing the result of some medical images which inputted into the system. The results showed that our system presents a promising performance and able to give relevant information related to the given image.
Data Hiding In Medical Images by Preserving Integrity of ROI Using Semi-Rever...IJERA Editor
Text fusion in images is an important technology for image processing. We have lots of important information related to the patient’s reports and need lots of space to store and the proper position and name which relates that image with that data. In our work we are going to find out the ROI (region of interest) for the particular image and will fuse the related document in the NROI (non-region of interest) of the image, till yet we have many techniques to fuse text data in the medical images one of form them is to fuse data at the boarders of the images and build the particular and pre-defined boarder space. We have propose an algorithm in which we first find out the area of interest and after that we find noisy pixels of the image to embed data in that noisy portions of the image. We use wavelets for smoothing images and segmentation process for extracting region of interest. Coordinates of the noisy pixels have been located and data has been embedded in those pixels .The used embedding technique embed data in least significant bits, hence does not degrade the quality of the image to unacceptable limits. Results show that it gives good PSNR and MSE values which are used for measuring quality effected performance.
The main objective of this work is the uniting and streamlining of an automatic face detection application and recognition system for video indexing applications. Human identification means the classification of gender which can increase the identification accuracy. So, accurate gender classification algorithms may increase the accuracy of the applications and can reduce its complexity. But, in some applications, some challenges are there such as rotation, gray scale variations that may reduce the accuracy of the application. The main goal of building this module is to understand the values in image, pattern, and array processing with OpenCV for effective processing faces for building pipe-lining, SVM models.
Development of durian leaf disease detection on Android device IJECEIAES
Durian is exceedingly abundant in the Philippines, providing incomes for smallholder farmers. But amidst these things, durian is still vulnerable to different plant diseases that can cause significant economic loss in the agricultural industry. The conventional way of dealing plant disease detection is through naked-eye observation done by experts. To control such diseases using the old method is extensively laborious, time-consuming and costly especially in dealing with large fields. Hence, the proponent’s objective of this study is to create a standalone mobile app for durian leaf disease detection using the transfer learning approach. In this approach, the chosen network MobileNets, is pre-trained with a large scale of general datasets namely ImageNet to effective function as a generic template for visual processing. The pre-trained network transfers all the learned parameters and set as a feature extractor for the target task to be executed. Four health conditions are addressed in this study, 10 per classification with a total of 40 samples tested to evaluate the accuracy of the system. The result showed 90% in overall accuracy for detecting algalspot, cercospora, leaf discoloration and healthy leaf.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in this felids, the paper proposed the face recognition as sensitive learning task experiments on a large face databases demonstrate of the new feature. The researcher recommends that there's a needs to evaluate the previous studies and researches, especially on face recognition field and 3D, hopeful for advanced techniques and methods in the near future.
An SVM based Statistical Image Quality Assessment for Fake Biometric DetectionIJTET Journal
Abstract
A biometric system is a computer based system and is used to identify the person on their behavioral and logical characteristics such as (for example fingerprint, face, iris, keystroke, signature, voice, etc.).A typical biometric system consists of feature extraction and matching patterns. But nowadays biometric systems are attacked by using fake biometric samples. This paper described the fingerprint biometric techniques and also introduce the attack on that system and by using Image Quality Assessment for Liveness Detection to know how to protect the system from fake biometrics and also how the multi biometric system is more secure than uni-biometric system. Support Vector Machine (SVM) classification technique is used for training and testing the fingerprint images. The testing onput fingerprint image is resulted as real and fake fingerprint image by quality score matching with the training based real and fake fingerprint samples.
This study is a part of design of an audio system for in-house object detection system for visually impaired,
low vision personnel by birth or by an accident or due to old age. The input of the system will be scene and
output as audio. Alert facility is provided based on severity levels of the objects (snake, broke glass etc) and
also during difficulties. The study proposed techniques to provide speedy detection of objects based on
shapes and its scale. Features are extraction to have minimum spaces using dynamic scaling. From a
scene, clusters of objects are formed based on the scale and shape. Searching is performed among the
clusters initially based on the shape, scale, mean cluster value and index of object(s). The minimum
operation to detect the possible shape of the object is performed. In case the object does not have a likely
matching shape, scale etc, then the several operations required for an object detection will not perform;
instead, it will declared as a new object. In such way, this study finds a speedy way of detecting objects.
For Image Authentication Problem using Encryption Technique and LDPC Source Coding is necessary in Content
Delivery via unsecure medium, Like Peer-To-Peer (P2P) File Sharing. These transferring Digital Files from one Computer to
another. Images are the Most Important Utility of our life. They are used in many applications. There are Two Main Goals of
Image Security: Image Encryption and Authentication. More different encoded versions of the original image available.In
addition, unsecure medium might tamper with the contents.. We propose an efficient, accurate, reliable process using
encryption and LDPC source coding for the image authentication problem. The key idea is to provide a Slepian-Wolf encoded
as authentication data which is encrypted using cryptography key before ready to send. The key used for encryption is usually
independent of the Plain-Image. This can be decoded with side information of an authentic image.
A Review on Various Forgery Detection Techniquesijtsrd
Nowadays, there barely exists any platform where digital images are not used. They are used in almost every field, namely digital media, electronic media, military, law, industry, forensics, and so on, and all over the internet. With such vast numbers of images, the importance of their authenticity has increased enormously .We give much importance to what we see on daily basis in newspapers, on the covers of magazines, social media such as Facebook, Instagram, Twitter and many more. Digital image manipulation is the act of distorting the contents of an image in order to fulfil some fraudulent purposes, such manipulations are known as forgeries. There exist various cases of image forgeries in history which caused clutter and affected people organizations. Earlier photographers were habituated with using the process of photomontage, in which composites of images were created by pasting, gluing to get the final print. However, due to evolution of technology, various tools have been developed by researchers and made available over the internet. Aarushi Thusu | Mr. B. Indra Thannaya "A Review on Various Forgery Detection Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49915.pdf Paper URL: https://www.ijtsrd.com/computer-science/computer-security/49915/a-review-on-various-forgery-detection-techniques/aarushi-thusu
A Review on Overview of Image Processing Techniquesijtsrd
Image processing is actually among the fast growing innovations across various areas of a business with applications. Image processing frequently forms key scientific areas within the areas of electronics and computer science. Image processing is a tool for refining raw photographs obtained in our everyday lives from rockets, ships, space samples or military identification flights. Thanks to technologically powerful personal computers, broad databases of current devices and the Graphic Technology and the accessible resources for such software and apps, this area is strong and common. The provided input is an image and its output an enhanced high quality image according to the techniques used in the image processing procedure. Image processing is typically called digital image processing, although it is often possible to optically process and analogy photograph. An overview of image processing methods is given in this article. This article focuses mainly on identifying specific methods utilized in various image processing phases. Hirdesh Chack | Vijay Kumar Kalakar | Syed Tariq Ali "A Review on Overview of Image Processing Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31819.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/31819/a-review-on-overview-of-image-processing-techniques/hirdesh-chack
EXPLOITING REFERENCE IMAGES IN EXPOSING GEOMETRICAL DISTORTIONSijma
Nowadays, image alteration in the mainstream media has become common. The degree of manipulation is
facilitated by image editing software. In the past two decades the number indicating manipulation of
images rapidly grows. Hence, there are many outstanding images which have no provenance information
or certainty of authenticity. Therefore, constructing a scientific and automatic way for evaluating image
authenticity is an important task, which is the aim of this paper. In spite of having outstanding
performance, all the image forensics schemes developed so far have not provided verifiable information
about source of tampering. This paper aims to propose a different kind of scheme, by exploiting a group of
similar images, to verify the source of tampering. First, we define our definition with regard to tampered
image. The distinctive features are obtained by exploiting Scale- Invariant Feature Transform (SIFT)
technique. We then proposed clustering technique to identify the tampered region based on distinctive
keypoints. In contrast to k-means algorithm, our technique does not require the initialization of k value. The
experimental results over and beyond the dataset indicate the efficacy of our proposed scheme
Facial Image Classification And Searching - A SurveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, face recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face recognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a marginal summary for future research and enhancements in face detection.
Analysis and Detection of Image Forgery Methodologiesijsrd.com
"Forgery" is a subjective word. An image can become a forgery based upon the context in which it is used. An image altered for fun or someone who has taken a bad photo, but has been altered to improve its appearance cannot be considered a forgery even though it has been altered from its original capture. The other side of forgery are those who perpetuate a forgery for gain and prestige. They create an image in which to dupe the recipient into believing the image is real and from this they are able to gain payment and fame. Detecting these types of forgeries has become serious problem at present. To determine whether a digital image is original or doctored is a big challenge. To find the marks of tampering in a digital image is a challenging task. Now these marks of tampering can be done by various operations such as rotation, scaling, JPEG compression, Gaussian noise etc. called as attacks. There are various methods proposed in this field in recent years to detect above mentioned attacks. This paper provides a detailed analysis of different approaches and methodologies used to detect image forgery. It is also analysed that block-based features methods are robust to Gaussian noise and JPEG compression and the key point-based feature methods are robust to rotation and scaling.
Encry-Pixel: A Novel Approach Towards Locational Privacy Enhancement in ImagesSoumyaShaw4
We attempt to mystify the metadata that includes the location coordinates to address the privacy concern. We put forward an algorithm that performs randomized location hopping to compromise the algorithms targeted to extract sensitive data. The idea is to hide sensitive information that might be used otherwise without user knowledge.
Elucidating Digital deception: Spot counterfeit fragmentVIT-AP University
An ordinary person always has confidence in the integrity of visual imagery and believes it without any doubt. But today's digital technology has eroded this trust. A relatively new method called image forgery is extensively being used everywhere. This paper proposes a method to depict forged regions in the digital image. The results for the proposed work are obtained using the MATLAB version 7.10.0.499(R2010a). The projected design is such that it extracts the regions that are forged. The proposed scheme is composed for uncompressed still images. Experimental outcome reveals well the validity of the proposed approach.
COVID-19 digital x-rays forgery classification model using deep learningIAESIJAI
Nowadays, the internet has become a typical medium for sharing digital images through web applications or social media and there was a rise in concerns about digital image privacy. Image editing software’s have prepared it incredibly simple to make changes to an image's content without leaving any visible evidence for images in general and medical images in particular. In this paper, the COVID-19 digital x-rays forgery classification model utilizing deep learning will be introduced. The proposed system will be able to identify and classify image forgery (copy-move and splicing) manipulation. Alexnet, Resnet50, and Googlenet are used in this model for feature extraction and classification, respectively. Images have been tampered with in three classes (COVID-19, viral pneumonia, and normal). For the classification of (Forgery or no forgery), the model achieves 0.9472 in testing accuracy. For the classification of (Copy-move forgery, splicing forgery, and no forgery), the model achieves 0.8066 in testing accuracy. Moreover, the model achieves 0.796 and 0.8382 for 6 classes and 9 classes problems respectively. Performance indicators like Recall, Precision, and F1 Score supported the achieved results and proved that the proposed system is efficient for detecting the manipulation in images.
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
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using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
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AHP validated literature review of forgery type dependent passive image forgery detection with explainable AI
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 11, No. 5, October 2021, pp. 4489~4501
ISSN: 2088-8708, DOI: 10.11591/ijece.v11i5.pp4489-4501 4489
Journal homepage: http://ijece.iaescore.com
AHP validated literature review of forgery type dependent
passive image forgery detection with explainable AI
Kalyani Kadam1
, Swati Ahirrao2
, Ketan Kotecha3
1,2
Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
3
Head SCAAI, Symbiosis International (Deemed University), India
Article Info ABSTRACT
Article history:
Received Aug 26, 2020
Revised Dec 28, 2020
Accepted Jan 19, 2021
Nowadays, a lot of significance is given to what we read today: newspapers,
magazines, news channels, and internet media, such as leading social
networking sites like Facebook, Instagram, and Twitter. These are the
primary wellsprings of phony news and are frequently utilized in malignant
manners, for example, for horde incitement. In the recent decade, a
tremendous increase in image information generation is happening due to the
massive use of social networking services. Various image editing software
like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many
others are used to create, modify the images and videos, are significant
concerns. A lot of earlier work of forgery detection was focused on
traditional methods to solve the forgery detection. Recently, Deep learning
algorithms have accomplished high-performance accuracies in the image
processing domain, such as image classification and face recognition.
Experts have applied deep learning techniques to detect a forgery in the
image too. However, there is a real need to explain why the image is
categorized under forged to understand the algorithm’s validity; this
explanation helps in mission-critical applications like forensic. Explainable
AI (XAI) algorithms have been used to interpret a black box’s decision in
various cases. This paper contributes a survey on image forgery detection
with deep learning approaches. It also focuses on the survey of explainable
AI for images.
Keywords:
Deep learning
Explainable AI
Image forgery detection
Image splicing
This is an open access article under the CC BY-SA license.
Corresponding Author:
Kalyani Dhananjay Kadam
Symbiosis Institute of Technology
Symbiosis International (Deemed University), Pune, India
Email: kalyanik@sitpune.edu.in
1. INTRODUCTION
Images are used in almost every field, such as medical systems, glamor, courts, military, and
industries, and social networking platforms such as Instagram, Facebook, and so on over the internet [1].
Regardless of whether it is the space shuttle exploding during launch, a man strolling on the moon, or officers
raising a banner on Iwo Jima during World War II, such ground-breaking images impact the society [1]. The
advancement in digital imaging software and photo-realistic graphics allows people to make images more
realistic or spread alternative meanings. Images can be fused, graphically improved, and created by
computers, then detecting these controlled images can be troublesome. The realness of digital images
becomes an important study area for research and development. It finds it difficult for forensic experts to
identify genuine and forged images. Identifying such manipulated parts performed by the faker’s
manipulation operation is a significant work. Detecting the forgery in digital images is one of the challenges
in the digital era. Nowadays, deep learning is gaining more attention due to its significant results. Deep
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Learning algorithms have achieved high accuracy, but offering drawbacks, as the important features cannot
be interpreted with the numbers which are given by deep learning model. The semantics is also not added.
These networks are giving a higher performance without the understanding of its inside working. Black box
issues are nothing but investigating the inner working of a deep learning model and interpreting why it
delivered a given yield. These problems become more critical when these networks are used in real-life
applications such as medical diagnosis or forensic. Explainable AI explains the decisions of a network. In
computer vision, visualization networks provide fascinating ways to visualize the image’s essential features,
interpreting the image. In this case, the input data is the image, and image interpretation is performed using
heat maps. Heat maps give the most significant input data areas for the interpretation. It allows the user to
understand which image pixels are associated with the expected class, and it also checks whether the deep
learning model focuses on the image’s reasonable area. Figure 1 shows the mind map of the article. The
article is mainly divided into digital image forgery detection, keyword analysis using various tools, deep
learning for handling forgery type-dependent forgeries, AHP model for image forgery detection, and
explainable AI.
Figure 1. Workflow of passive image forgery detection with a focus on explainable AI
2. DIGITAL IMAGE FORGERY DETECTION
Image forgery detection is classified into two approaches [1]: passive or blind and active approach.
Various types of image forgeries are shown in Figure 2.
Figure 2. Digital image forgery detection
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2.1. Active approach
In this approach, a computerized signature or watermark is injected in an image, and that is inserted
either by an individual or by the acquisition device. Digital watermarking inserts particular information
known as a message digest inside the image while capturing it. This information is then taken out from the
image at later phases to get its authenticity. This extricated information is checked to see if it varies or not; if
it changes, it shows that the image was altered after the image capturing procedure. It is a two-phase process,
in the first phase, the message-digest is embedded in the image. In the second phase, i.e., after reaching the
image to its s destination, the message digest is retrieved and compared with the obtained watermark [1].
Digital watermarking is an effective method to secure the image trustworthiness; however, different
difficulties make its utilization unrealistic. Few cameras and gadgets have the property to insert watermark in
the image at the time of creation. Some types of equipment, e.g., Canon EOS-1D, Nikon D2Xs, have
embedding features but are overpriced. The disadvantage is that this method cannot differentiate legitimate
and invalid operations in the image. Legitimate operations are performed for improving image quality, such
as sharpening, contrast improvement, and so on. In the case of watermarking special software or hardware is
needed to insert message digest in an image. Image forgery is identified with statistical information. This is
done by dividing the image into similar region characteristics; after this, statistical measures such as mean,
mode, median, and range of pixel values are inserted in the image with an encryption method [1]. Then this
information is verified to check its authenticity.
2.2. Passive approach
Passive methodologies [1] uses statistical information of the image to detect forgeries in an image.
These methods work without earlier information about the image, for example, digital watermarks or
signatures. This method uses the existing image, and the image’s manipulation operation changes its
properties. It will make the image inconsistent, and thus the statistical image information is used for forgery
identification. It is further classified into forgery type independent and dependent.
2.2.1. Forgery type independent
These techniques are used to identify the forgeries based on artifact clues left during retouching and
lighting conditions [1]. This type of forgery uses statistical data of the image, and such invisible information
is misused in different image handling activities, e.g., jpeg confining, image filtering, contrast improvements,
and resampling. The resampling process changes the image by performing upsampling and downsampling
operations and tools handling such forgeries works on either pixel or frequency domain. The median filtering
tool is mostly used for noise elimination and image improvement. These methods give excellent
performance, while the images are uncompressed and large. Commonly, the images are compressed with the
JPEG compression strategy. The main goal of JPEG-based forgery identification techniques is to find out the
locations of images with different JPEG compression locations.
Forgery type dependent
This type of forgery focuses only on specific kinds of forgeries such as copy move and image
splicing; these are gaining more attention as they are commonly performed manipulations on the image.
a. Copy move
This type of forgery copies part of the image and pasted into the same image at another region,
which entirely changes its meaning. This is usually done to make an object “disappear” from the image by
hiding it with a segment copied from another part of the image. This kind of forgery is very easy to perform,
but it is very hard to detect as the copied section comes from the same image. Its different properties, such as
shade palette, noise component, and other characteristics, are suitable to the rest of the image, which will not
be recognized via the same methods that find inconsistencies in statistical measures in another portion of the image.
Figures 3 and 4 show that numerous papers are published in this exploration area from various
countries. Data analysis is performed by using queries such as ‘copy move forgery’ and ‘copy move forgery
detection using deep learning’ on IEEE (ieeexplore.org), Scopus (scopus.com), WOS (web of science), and
ACM databases. The duration from 2001 to 2020 is considered for getting the result. Three different steps are
performed for copy move forgery detection (CMFD): separating the image into blocks (overlying/non-
overlying), executing the image property extraction technique on each block, comparison of features. In
CMFD methods [1], the principal objective is to compare the portions inside an image and recognize the
replicated and the original region.
b. Image splicing
Image compositing or splicing joins more than two images to create a forged image. It can be done
by various software tools such as Adobe Photoshop, and Coral PaintShop. The copy move detection methods
cannot be used in splicing as it works on multiple images; the manipulated part of the image has distinct
properties compared with the rest of the image. This method uses numerous properties such as DCT and
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DWT coefficients, Bi-coherence properties, camera response operation, and invariant image moments, The
splicing methods consider the gadgets inborn properties instead of capturing the gadget’s actual attributes.
Figures 5 and 6 show that numerous papers are published in this exploration area from various countries.
Data analysis is performed by using queries such as ‘image splicing’ and ‘image splicing detection using
deep learning’ on IEEE (ieeexplore.org), Scopus(scopus.com), WOS(Web of Science), and ACM. The
duration from 2001 to 2020 is considered for getting the result.
Figure 3. Last 19 years of publication data for ‘copy
move forgery’ in scopus, IEEE, WOS (web of
science), and ACM
Figure 4. Past 19 years publication count for ‘copy
move forgery detection using deep learning’ in
Scopus, IEEE, WOS (web of science), and ACM
Figure 5. Past 19 years of the publication information
for ‘image splicing’ in Scopus, IEEE, WOS (web of
Science), and ACM
Figure 6. Last 19 years of publication information
on ‘image splicing detection using deep learning’ in
Scopus, IEEE, WOS (web of science), and ACM
3. KEYWORD ANALYSIS USING WORD CLOUD, SCIENSCAPE AND LEXIMANCER TOOL
Figures 7-9 indicate the top 3 paper’s word clouds in a deep learning approach for image forgery
detection and explainable AI [2]. Word cloud highlights important words in this research field. The words
with smaller fonts are an indication of future research. All the word clouds are drawn from www.wordclouds.com.
Important words are copy move, splicing, deep learning, forgery detection, localization of forgery, segmentation,
classification, visualization, explainable, and XAI. Less important words are image tampering detection,
tampered, manipulation, blocks, and. Figures 10 and 11 are drawn with the ScienScape tool, which shows the
top keywords per year from Scopus and the WOS database. In Figure 12, the thematic graph drawn using the
Leximancer tool uses a machine learning technique that finds the relation between different author keywords.
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Figure 7. Word cloud for “a deep learning approach
to detection of splicing and copy-move forgeries in
images
Figure 8. Word cloud for “image region forgery
detection: A deep learning approach
Figure 9. Word cloud for “visual interpretability for deep learning: a survey∗”
Figure 10. Keywords per year (top keywords per year) for scopus database
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Figure 11. Keywords per year (top keywords per year) for WOS database
Figure 12. Thematic diagram for keywords
4. DEEP LEARNING APPROACH FOR COPY MOVE AND IMAGE SPLICING FORGERY
DETECTION
ConvNets or convolutional neural networks (CNNs) [3] is one of the main categories for image
recognition and classifications in neural networks. Face recognition, and detection of objects in an image are
some of the fields where CNNs are extensively used. CNN is useful for retrieving meaningful features for
image classification. This model [3] uses CNN for identifying copy move and image splicing forgery in an
image. This network is pretrained with the help of labeled images from the training image dataset. The same
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network is then used for extracting the features for the patches, and these features are combined to train the
SVM model. In [4], a two-phase deep learning approach is employed to learn the features for detecting image
forgery for an image that comes in various formats. In the first stage, Stack AUTOENCODER is used to
learn complex features from each patch. In the second stage, the image is broken into patches of 32x32. From
each patch, the contextual data is used for finding tampering. In future work, deep belief networks can be
used for feature learning. This work [5] presents a technique for tampering detection and recognizing the
manipulated regions for the images delivered through the combination of images taken from various camera
models. The image is broken into color patches of 64x64. In this, CNN is used to extract features from every
patch, and iterative algorithms are used on these features to group them into pristine clusters and forged
clusters. Future work focuses on the usage of CNN to learn more features such as blurring, rotations, and
resizing. This work [6] uses two different methods for tampering detection and localization. The first method
calculates resampling features over overlying areas. A deep learning network and Gaussian conditional
random field are utilized for creating the heatmap. In the next method, the resampling features are then
passed to the LSTM model to classify the regions as either forged or not. In the future, CNN and LSTM
fusion can be used for forgery detection.
Convolutional neural network (CNN) [7] identifies the image tampering with automatic feature
learning. This model has five convolutional layers. The network is made up of two fully connected layers and
a softmax classifier. In this [8], the CNN network is employed for determining patch-based inpainting
operation. CNN is utilized for learning the features of the image. CNN model is trained with a class label
matrix of all the pixels of the image. In this weighted cross entropy is used to balance the inpainted and
uninpainted pixels. CNN encoder-decoder network is employed for predicting the inpainting probability of
each pixel in an image. In this work [9], illuminant maps and CNN are used for the splicing identification.
The deep and transfer learning techniques are utilized for extracting features for forgery identification. The
classifier is trained with these features. After identifying the image, whether it is fake or not, the next step is
to locate the manipulated region. The future work considers localization maps for the identification of forged
regions in an image. In [10], a fully convolutional network is used for detecting the image splicing. The
Single task FCN is trained with a surface label that identifies every pixel in the image as real or not. SFCN
generates a coarse localization output. MFCN is trained on the superficial label and boundary, which shows
that the pixel is related to the spliced region’s border. The edge enhanced MFCN uses a surface label and
edge probability map, which is better than SFCN and the MFCN approach. In this work [11], CNN based
methods are used for handling copy move forgery detection. CNN with CFA (color filter array) features are
used for finding and localizing the manipulated region. The CFA interpolation technique develops
interrelationship and consistency between the pixels. Therefore, inconsistency in manipulated areas can be
used for identifying the forged regions.
R-CNN [12] is employed for finding manipulated regions in forged images. This network provides
two streams; one is RGB in which features are extracted from RGB image to detect tampering such as
unnatural altered boundaries, and strong contrast difference. The second is the noise stream, which takes
advantage of obtaining noise features and identify the inconsistency with real and manipulated regions. The
bilinear pooling layer is then used for fusing these features retrieved from two streams. In this [13], deep
neural network and conditional random field are used to identify the image’s spliced region, which does not
need any prior data. Three unique variations of FCN were utilized to improve the accuracy. Discovery of
small manipulated objects is difficult as down-sampling action reduces the real image measurements and
makes smaller objects considerably harder to identify. Another issue is the overfitting issue, which will be
addressed in future work. Another future work includes the optimization of the network for splicing
detection. This framework [14] is used for the detection of forged images. In this network, the image is split
into patches. Then resampling features are extracted from these patches. The Hilbert curve finds out the
sequencing of patches which are supplied to LSTM. The sampling features detect the correlation between
patches. The encoder is used for finding out the spatial location of a manipulated region. Spatial features
from the encoder and out features from LSTM are combined for detecting forgery in an image. The decoder
model gives the finer representation of the spatial map, which offers the altered region in an image. An
improved mask R-CNN model [15] (regional convolutional neural network) is proposed with a Sobel filter to
recognize the altered and unaltered region’s distinctive features. This network handles two types of forgeries,
such as copy move and splicing. Pixel wise information is used for training the model, and Sobel filters use the
edge’s information to identify the manipulated boundaries. Fully convolutional network model (FCNN) [16] is
used for image splicing detection. It distinguishes the altering of an image as well as recognizes the forgery
of spliced regions. The FCN gives misclassification when there are numerous people are there in the non-
spliced region of an image. The improved FCN is used to capture the different features of spliced and non-
spliced regions. The improved CNN network is trained with authentic and altered images.
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5. ANALYTICAL HIERARCHICAL PROCESSING (AHP) MODEL FOR IMAGE FORGERY
DETECTION
Analytical Hierarchical Process is a multi-criteria decision-making mathematical technique. It helps
to make associations among qualitative and quantitative attributes and is almost used in every area. In this
paper, the AHP calculation is done by using an online tool (https://bpmsg.com/ahp/). This process follows
five steps, which are given below: i) Hierarchy modeling, ii) Priorities establishment; iii) Decision related to
overall priorities of hierarchy; iv) Checking consistency; and v) Judgment preparation.
In AHP, numerical ranking [17] ranging from 1 to 9 compares attributes at various levels in the
hierarchy by considering alternatives. Figure 2 decides different levels of decisions for the analytical
hierarchy process of digital image forgery detection. Literature shows that passive image forgery detection is
more significant than the active approach; therefore, in level 1 passive approach is used to make a decision.
After looking at hierarchy modeling, Tables 1-3 indicates prioritizing attributes for performing AHP on
image forgery. The color code in columns P and Q of Table 1-3 shows the number of comparisons that must
be made between P and Q. By establishing relevance with scale (1-9), the yellow color indicates significance
among the compared attributes. Table 4-6 show consistency checks for considered levels of a first and second
set of important image forgery detection attributes. These tables show the priority and rank of a group of
important attributes, with the highest priority indicating the most important attribute.
In the second level, the forgery type-dependent technique is considered. Literature shows that these
forgeries emphases only on particular types of forgeries, and these are obtaining more recognition as they are
frequently performed manipulations in the image. Figures 13-15 show rank levels 1, 2 and 3, respectively.
Figure 16 summarizes the AHP implementation result for the undertaken research problem, which gives
significant attributes at every level.
Table 1. Formulation of priorities for level 1(types of forgery detection such as active and passive approach)-
first set of significant attributes for image forgery detection (1 comparison)
First set of attributes Equally important How much important?
P Q 1 2 3 4 5 6 7 8 9
1 Passive Approach Active Approach
Table 2. Consistency check for level 1-the first set of significant attributes for image forgery detection
Consistency check for level 1-the first set of
significant attributes
+ - 1 2
Priority Rank
Passive Approach 83.3% 1 0.0% 0.0% 1 5.00
Active Approach 16.7% 2 0.0% 0.0% 0.20 1
Comparisons 1 Principal
eigenvalue
2.000
Consistency check 0.0% Eigen vector
solution
1 iterations,
delta
7.7E-34
Table 3. Formulation of priorities for level 2 (types of passive forgery detection such as forgery type-
dependent and forgery type independent)-the second set of significant attributes for image forgery detection
(1 comparison)
Second set of attributes Equally important How much important?
P Q 1 2 3 4 5 6 7 8 9
1 Forgery type dependent Forgery type independent
Table 4. Consistency checking for level 2-the second set of significant attributes for image forgery detection
(1 comparison)
Consistency check for level 2 – the second set
of significant attributes
+ - 1 2
Priority Rank
Forgery type dependent 87.5% 1 0.0% 0.0% 1 0.142857
Forgery type independent 12.5% 2 0.0% 0.0% 7 1
Comparisons 1 Principal
eigenvalue
2.000
Consistency check 0.0% Eigen vector
solution
1 iterations,
delta
0.0E+0
In the third level, equal priorities are given to both copy move and splicing
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Table 5. Formulation of priorities for level 3 (types of forgery type-dependent such as copy move and
splicing)-the third set of significant attributes for image forgery detection (1 comparison)
Third set of attributes Equally important How much important?
P Q 1 2 3 4 5 6 7 8 9
1 copy move splicing
Table 6. Consistency checking for level 3-the third set of significant attributes for image
forgery detection (1 comparison)
Consistency check for level 3-the third set of significant
attributes + - 1 2
Priority Rank
Copy move 50.0% 1 0.0% 0.0% 1 1.00
Splicing 50.0% 1 0.0% 0.0% 1.00 1
Comparisons 1 Principal eigenvalue 2.000
Consistency check 0.0% Eigen vector
solution
1 iterations,
delta
0.0E+0
Figure 13. Rank level 1-the first set of significant attributes for image forgery detection
Figure 14. Rank level 2-the second set of significant attributes for image forgery detection
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Figure 15. Rank level 3-the third set of significant attributes for image forgery detection
Figure 16. Summarization of AHP implementation result for the undertaken research problem
6. LITERATURE SURVEY ON EXPLAINABLE AI
Machine Learning is frequently used in research and development and, mainly through Deep
Learning and neural network achievement. Nowadays, machine learning networks are overgrowing for
making predictions in critical contexts; different AI stakeholders require transparency in predictions [18].
This is achieved by utilizing interpretable networks or growing new strategies and using a local
approximation for interpretation. XAI helps the clients to understand and trust a network using various
interpretable networks.
AI exhibited to be important in discovering new uses for existing drugs, detecting cancer in tissues,
distinguishing heart arrhythmia, and anticipating hypoglycemia in people with diabetes at an earlier time than
that of the clinical industry. Machine Learning, Artificial Intelligence, Neural Networks, and Deep Learning
models have recently shown their medical field performance, but justifications assisting the outputs of a
network remain significant, e.g., in the precision medicine field. Deep Learning is also used in other domains,
e.g., self-directed vehicles in transport systems, safety, and economics. Humans using these networks must be
trustworthy, interpretable, and tractable. Numerous research and development papers show that XAI has
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become an essential field in machine learning, which proposed various procedures and systems that specify
understandability in various fields.
6.1. XAI in deep learning
Numerous work is presently done in the explainable AI area, but several difficulties exist in
explaining deep learning networks. The difficulties such as different challenges in feature selection, feature
significance, and visualization methods. There is no consistency in heatmaps, saliency maps and masks,
neuron stimulations, and other techniques similar to visualization methods.
This network [19] is practically a simple network based on the convolutional decomposition of
images under a sparsity check, and this is an unsupervised approach. This method learns rich feature sets by
developing a hierarchy of such decompositions. Applying this model to the natural image generates different
sets of filters that capture the edge primitives such as parallel edges, edge connections, parallel lines, curves,
and basic geometric shapes and high-order image structures. This does not require settings of different
parameters of CNN models such as max-pooling, local contrast normalization. This work [20] presents two
visualization methods for deep classification ConvNets. The first visualization method produces an image,
which is illustrative of a class of interest. The second method calculates a saliency map, specifically for a
given image. It also generates a class, highlighting the areas of the given image. Such a saliency map can be
utilized to initialize GraphCut based object segmentation without the need to train devoted segmentation or
detection networks. In the future, this method plans to incorporate the image’s explicit saliency maps into the
learning process in a more principled way. This method [21] generates sharper visualizations of colorful
image regions than the earlier methods specified in literature and can be utilized even in a lack of maxima in
max-pooling regions. This work [22] combines bottom-up and two top-down attention. In the object level
attention, various patches are given to the system, which selects significant patches to a particular object. The
part level attention focuses mainly on local discriminate patterns. This method combines these attentions to
train domain-specific deep networks. This technique [23] explored large convolutional neural networks
trained for image classification using different ways. This network gives a way to visualize the internal
representation of a network. This helps to identify the problems within the network. In this paper, a series of
occlusion experiments are conducted for image classification problems, which shows the local structure is
more sensitive than its broad scene. In [24], the authors proposed a method that shows single pixels’
visualization using a heatmap in result prediction. In this, a layer-wise relevance propagation (LRP) method
is used, which depends on a Taylor series to estimate the point rather than partial derivatives to estimate that
point. This paper [25] performs a sensitivity study of one layer CNN that determines different network
components affecting its performance. It aims to differentiate between essential and reasonably
inconsequential design decisions for sentence classification.
The technique used in this paper [26] can invert images represented in HOG format. This technique
shows that few layers in CNNs hold photographically the same data of the image through geometrical and
photometric invariance steps. This technique shows that continuously increasingly invariant and abstract
information of an image is visible in the model. This method [27] utilized the CNN-LSTM model for the
generation of image captions. In this technique for capturing multiple objects inside an image, features are
extracted from the lower convolutional layers. Thus, multiple features represent a single image at different
localities. The LSTM is trained with features that are extracted from images from various localities. LSTM
generates word, and this process is repeated for k-times for creating K-words for image caption.
In this paper, layer-wise relevance propagation (LRP) [28] technique is compared with deep
convolutional neural networks and fisher vector classifiers. LPP technique is used for calculating results for a
single part of a given image, signifying its impact on the estimation of the classifier for one particular
assessment point. Gradient-weighted class activation mapping (Grad-CAM) offers a new way to recognize
any CNN-based model. Deep neural networks (DNN) show various pattern recognition tasks, particularly in
vision classification problems. DNNs [29] performance is improved by understanding the brain’s internal
working. One such method for enhancing DNN is an activation maximization, which works on the principle
of neuron’s activation for recognizing any image. Activation maximization is improved by using deep
generator network (DGN). This algorithm produces synthetic images that look almost real. Secondly, it
discloses the features learned by every neuron in an interpretable way. Thirdly it generalizes to new datasets.
And lastly, it tends to be considered as a high-quality generative technique. The proposed network [30]
emphasizes the object’s selective features and predicts a class label suitable for that particular image. This
method provides a loss function, which generates a collection of meaningful words with the help of global
sentence property such as class specificity.
This paper [31] proposed a method that stacks various attention modules. The advantage of this
method is that distinct attention modules hold various types of attention for feature learning. Future work
focuses on detection and segmentation areas. This paper [32] shows two ways to clarify deep learning
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networks. The first way focuses on the sensitivity estimation based on the variations in the input. The second
way expressively divides the decision based on the given input. The future work will study the hypothetical
establishments of interpretability. It also focuses on the relationship between generalizability, compactness,
and interpretability. This method [33] involves three steps; in the first step global-and-local attention
(GALA) module is utilized for learning combinations of local saliency and global relevance variations in
feedforward neural networks. GALA adjusts input feature maps with an attention mask of a similar
dimension as the input. Secondly, a vast scale online analysis was defined to enhance ImageNet. Lastly,
identification accuracy is improved by humans. Humans can administer attention and only concentrate on the
visual features that people favor object identification. This paper uses a technique that endeavors to highlight
conditions between successive layers in a CNN to get discriminative pixel areas that control its prediction. In
this technique, CNNs are trained for different computer vision undertakings (for example, image captioning
and image identification) can reliably confine the objects in an image with minimal extra calculations
compared to gradient-based techniques. This method can be used across various types of networks and
different data or information modalities.
7. CONCLUSION
In this paper, the survey on image forgeries with the deep learning approach is presented. It also
focuses on the survey of XAI for images. Literature shows deep learning frameworks have higher
performance accuracy, but the results are not interpretable because of which there is a need for techniques
that interpret decisions either in visual format or in wordings. The explainable artificial intelligence survey
focuses on various types of XAI techniques in deep learning frameworks that help to interpret the decision.
For image forgery detection, three different databases, namely Scopus, Web of Science, and ACM Digital
Library, have been used, which shows the number of papers published in this area from various countries.
Keyword analysis for image forgery detection is done with the help of various tools such as word cloud,
Sciencescape. The thematic graph using the Leximancer tool studies relationship between different author
keywords. The words with smaller fonts indicate future research in forgery detection and explainable AI in
word cloud figures. The figures drawn with the Sciencescape tool’s help provide top keywords per year in
forgery detection. The AHP model is carried out on digital image forgery detection at multiple levels. The
digital image forgery detection AHP model is based on existing literature. This survey can be used by various
research scholars, technologists, and experts working in multiple fields where image and interpretation have
more significance. Till date, no research paper considered the combined scenario of image forgery with
explainable AI; this is the key aspect of this research paper.
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