Image Forgery Detection Research Papers (original) (raw)

2025, iJournals: International Journal of Software & Hardware Research in Engineering

Stroke is one of the leading causes of mortality and disability. It can result in complications like facial paralysis which impair communication and emotional expression. Stroke cases are rising in India due to hypertension, diabetes, and cardiovascular diseases. There is an urgent need for affordable diagnostic tools, particularly in rural areas where healthcare access is limited. Diagnostic methods like clinical examinations and imaging systems, are either subjective, time-intensive, or expensive. This study focuses on addressing these challenges by developing a convolutional neural network (CNN) for detecting stroke-induced facial paralysis. The CNN model was trained on a dataset of 4,224 labeled images which were categorized into six classes representing different facial regions affected by paralysis along with severity. The CNN model consisted of multiple convolutional layers for extracting features. Max-pooling layers were used for reducing dimensionality, and dropout layers were used to avoid overfitting. The trained model achieved a validation accuracy of 97.04% with a validation loss of 0.0556. Its confusion matrix demonstrated the model's accuracy in classifying samples across various classes. A Streamlit-based web application was developed to allow users to upload or capture images of their face for detecting facial paralysis symptoms that indicate stroke. The app also recommends on the basis of severity of facial paralysis detected in the captured or uploaded image. The results highlight the developed model's practical utility in early stroke detection. This model reduces diagnostic costs and enables accessibility for people living in areas that are underserved. This model can empower individuals with a user-friendly solution for effective stroke management.

2025, Lecture Notes in Computer Science

In this paper, a detailed evaluation of multi-scale Weber local descriptors (WLD) based image forgery detection method is presented. Multiscale WLD extracts the features from chrominance components of an image, which usually encode the tampering information that escapes the human eyes. The WLD incorporates differential excitation and gradient orientation of a center pixel around a neighborhood. In the multi-scale WLD, three different neighborhoods are chosen. A support vector machine is used for classification purpose. The experiments are conducted on three image databases, namely, CASIA v1.0, CASIA v2.0, and Columbia color. The experimental results show that the accuracy rate of the proposed method are 94.19% for CASIA v1.0, 96.61% for CASIA v2.0, and 94.17% for Columbia dataset. These accuracies are significantly higher than those obtained by some state-of-the-art methods.

2025

Copy-Move Forgery Detection (CMFD) method is useful for identifying copy and pasted portions in an image. CMFD has demand in forensic investigation, legal evidence and in many other fields. In this paper, the gists of different newly arrived methodologies in current literature are discussed. Some existing methodologies can be able to localize the forged region and some are not. An efficient method for localization of copy move forgery is proposed in this work for identifying forgery. In the proposed methodology, CMFD is achieved by giving suspected image to Steerable Pyramid Transform (SPT), Local Binary Pattern (LBP) is applied on each oriented subband obtained from SPT to extract feature set, then it is used to trained Support Vector Machine (SVM) to classify images into forged or not. Then localization process is carried out on forged images. Results of proposed methodology are showing robustness even though the forged image has undergone some post processing attacks viz., rotation, flip, JPEG compression.

2025, iJournals: International Journal of Software & Hardware Research in Engineering

Feeding difficulties are one of the most significant concerns of children with cerebral palsy. Their poor posture, muscle tone, and movement control make them more susceptible to choking and highly reliant on caregivers during feeding time. Current feeding solutions include gastrostomy and nasogastric tube insertions. These have relieved a good number of symptoms but are associated with risk in the form of infection, skin irritation, and inhibited mobility for the child. Some studies have reported that as many as 35% of children with CP suffered from these complications. Thus, there is an immediate need for a safer, non-invasive feeding solution that enhances autonomy while maintaining caregiver oversight. To solve these problems, the paper introduces a new robotic feeding assistant for children suffering from CP, FeedEase, through which facial landmarks can be detected and analyzed by using MediaPipe computer vision technology to establish, in real time, if the child's mouth is open or closed. FeedEase is controlled by the Arduino Nano and fed by precise NEMA 17 stepper motors through DRV8825 drivers, and it dispenses food automatically only when the mouth is open, thus reducing the chances of choking significantly and thus hands-on intervention by a caregiver or caregiver. In initial simulations, FeedEase has shown impressive accuracy as pointed out during in-mouthstate detection measures exceeding 95% precision and applicable both in home care scenarios and places like institutional care. The system is designed so that the caregivers will hardly intervene, allowing the young ones with CP to be more independent during meals. Earlier simulations performed quite well, achieving over 90% accuracy on mouth-state detection, thus making FeedEase applicable for both domestic and institutional care. This investigation adds on to the avowal nursing care by feeding efficiency via facial expression recognition, and also provides a novel way of recognizing hunger cues .Effectively, combining and involving machine learning approaches with computer vision in real time, FeedEase offers an easier and non-invasive mode of feeding, hence increasing independence, minimizing the caregiver's burden and enhancing the quality of life for a child suffering from cerebral palsy.

2025, International Journal of Advance Research, Ideas and Innovations in Technology

License plate recognition (LPR) has always been one of the crucial predicaments faced due to in numerous reasons such as severe lighting conditions, complex background, unpredicted weather conditions, low light and more. This paper is to enlight the above-mentioned muddles through a model based on OpenCV for enhancing details of edge information in license plate with improved text detection and recognition methods. LPR is a cutting-edge, next-generation system with imminent technological application in almost every field of the transportation industry.

2025

Numerous aspects of daily life contribute to societal stability, and the security of people's perceptions of the worldonline is one target of various malicious attacks. Professional forgers can now quickly create copy-move, splice, orretouch photos with the use of today's advanced tools. It has been determined that splicing, is a widespread methodof manipulating images. Image forgery can also lead to substantial setbacks and challenges, some of which may havesignificant ethical, moral, or legal consequences. Thus, the paper proposes a system that combines SD-LBP (StandardDevision-Local Binary Pattern) based passive picture splicing detection system and ANN classifier. The SD-LBP iscreated to have the benefits and avoid drawbacks of Local Binary Pattern (LBP). The SD-LBP extraction is typicallyperformed by employing proposed SD value-based thresholding instead of the center pixel, which is robust to noiseand other photometric attacks. The second part of the proposed system is the ANN classifier is used that extract thefeature of images to lower the error and build a model that can tell spliced images from real photos that have beendigitally altered. The proposed system is creating a reliable image forgery detection technique that was implementedwith CASIA V2.0 standard dataset. The results showing that it outperformed compared with other methods on the interms of accuracy (97.8%), sensitivity (98.6%), and specificity (97.1%). Most importantly, the proposed SFD methodexceeded the state-of-the-art efforts in this field in terms of accuracy.

2025, International Journal of Electrical and Electronics Research

Digital forensics and computer vision must explore image forgery detection and their related technologies. Image fraud detection is expanding as sophisticated image editing software becomes more accessible. This makes changing photos easier than with the older methods. Convolution LSTM (1D) and Convolution LSTM (2D) + Convolution (2D) are popular deep learning models. We tested them using the public CASIA.2.0 image forgery database. ConvLSTM (2D) and its combination outperformed ConvLSTM (1D) in accuracy, precision, recall, and F1-score. We also provided a related work on image forgery detection models and methods. We also reviewed publicly available datasets used in picture forgery detection research, highlighting their merits and drawbacks. Our investigation revealed the state of picture fraud detection and the deep learning models that worked well. Our work greatly impacts fraudulent photo detection. First, it highlights how important deep learning models are for picture forgery ...

2025

15. NUMBER OF PAGES 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 5c. PROGRAM ELEMENT NUMBER 5b. GRANT NUMBER 5a. CONTRACT NUMBER Form Approved OMB NO. 0704-0188 3. DATES COVERED (From -To)

2025, Studi Piemontesi

Jeanne Baptiste d’Albert nacque a Parigi nel 1670, figlia di Louis Charles «duc de Luynes et de Chevreuse, pair de France, chevalier des ordres du Roi et grand-fauconnier de France » e della sua seconda moglie Anne de Rohan-Guéméné. Fu battezzata nella chiesa di Saint-
Sulpice, padrino il secrétaire d’État au Commerce et à la Marine
Baptiste Colbert, madrina la maîtresse en titre di Luigi XIV Anne-Julie de Rohan-Chabot, principessa Soubise. Fu educata nel cuore del giansenismo parigino, in quell’abbazia femminile di Port-Royal nel cui
chiostro si incrociavano suore ferree nella regola cistercense e scrittori, filosofi, moralisti che si chiamavano Arnauld, Lancelot, La Rochefoucauld, Racine, Le Maistre de Sacy, Pascal, Hamon, La Fontaine

2025, INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS

A number of methods for altering faces in films have been effectively created and made publicly accessible in recent years (e.g., Face Swap, deepfake, etc.). Using these technologies, it is possible to facilitate face video modifications with inaccurate results. It is employable in almost all fields. However, the overemphasis of all technologies is fatal prone to have a certain effect in society which may be negative (e.g., fake news, and cyberbullying revenge porn). It is therefore important to be able to tell whether a person's face in a video has been modified, subjectively. To be able to address the problem of deepfake videos, we focus on the problem of face alteration detection in video sequences. In particular, we focus on the ensembles of several Convolutional Neural Network (CNN) models that have been developed.The proposed methodology attains these objectives through the use of attention layers and data training powerful models derived from a base network, EfficientNetB4. By using two publicly available datasets and combining over 119,000 videos, we show how to be able to address these bezier curves, Detecting face alteration is a crucial field of computer vision remains a challenging task in most scenarios, but we demonstrate that in our case, the combined networks approach highly improves the results.

2025, iJournals: International Journal of Software & Hardware Research in Engineering ISSN:2347-4890

The Mutual Information is an important statistic which quantifies the relation between two probability distributions in terms of the mutual information content across the two (parallel) linguistic corpuses under consideration. This is a significant conceptual model which forms the basis of techniques which explore the correspondence of the parallel corpuses for similarity and translation. The traditional formulation of Mutual Information is useful in this regard to model parallel corpus instances and obtain inferences useful for corresponding translations. However, the inherent lacuna in this scheme is the loss of generality due to reliance on specific corpus instances and thereby failing/underperforming with respect to realistic precision and reliability of the inferred model parameters for general use. This work is an attempt to address this lacuna by introducing a novel formulation for (True) Mutual Information which is robust from being affected by the loss of generality (condition) mentioned above. The novel formulation recognizes the fact that the (True) MI statistic can be obtained as measure of deviation of the individual probabilities of the distribution from critical values (as will be elaborated) , which are obtained by computing the Takens embedding of the distribution. The resultant measure is robust in generality as the Takens embedding captures how the distribution tends to move (in time), thus providing a Truer picture which is independent of the specifics of the choice of particular corpus instances..

2025

Home invasion crimes in South Korea, though relatively rare, still occur, particularly in urban areas like Seoul, where property crimes such as burglary and robbery are more prevalent. Despite low violent crime rates, the rise in property crimes has prompted many households to adopt advanced security systems, including CCTV cameras. The police respond quickly to such incidents, often using surveillance footage and data analysis, although the lack of physical evidence or witnesses can complicate investigations. This paper proposes a smart security system that combines YOLO for real-time weapon detection and RNN-family models (RNN, LSTM, GRU) for processing emergency messages. Evaluation results demonstrate the system's effectiveness in detecting weapons, tracking intruders, and generating timely reports, showcasing the potential of deep learning techniques to enhance home security. By integrating advanced object detection and message analysis, the proposed system offers a promising solution to improve response times and reduce the risks associated with home invasions.

2025, Positif Journal

The development of picture editing software over the past several years has led to the establishment of a topic of active research in
the field of digital image fraud detection. Passive forgery detection, or Copy Move Forgery Detection (CMFD), is the focus of this work. Using the copy move approach, it is applied to photographs that have been altered. The proposed feature extraction method for a CMFD technique that uses 2 Nearest Neighbor (2NN) with Hierarchical Agglomerative Clustering (HAC) as the feature matching method is Oriented Features from Accelerated Segment Test and rotated Binary Robust Independent
Elementary Features (Oriented FAST and rotated BRIEF). It is suggested that this method be used for CMFD. The suggested CMFD method was tested on pictures that were subjected to different geometrical attacks at different times. The suggested approach for evaluations, which utilizes
images from the MICC-F600 and MICCF2000 databases, can achieve an overall accuracy rate of 84.33% and 82.79%, respectively. When applied to photos that had been manipulated in a number of ways, such as rotated, magnified, or object translated, the True Positive Rate for forgery
detection was above 91%.

2025, International Journal of Gender, Science and Technology

One of the most significant methods utilized in the deep learning approach is text recognition. Text recognition is now a very significant activity that is utilized in many applications of current gadgets to recognize images in a detailed manner. Automatic Number Plate Recognition, for example, is an image processing approach that detects the vehicle's number (license) plate. The Automatic Number Plate Recognition system (ANPR) is a key feature that is used to manage traffic congestion. The goal of ANPR is to devise a method for automatically identifying permitted vehicles using vehicle numbers. Automatic Number Plate Recognition (ANPR) is utilized in a variety of applications, including traffic control, vehicle tracking, and automatic payment of tolls on roads and bridges, as well as monitoring systems, parking management systems, and toll collecting stations. The established approach first recognizes the vehicle before taking a picture of it. After that, the number plate region in the car is localized using a Neural Network, and the image is segmented. Using a character recognition approach, characters are retrieved from the plate. The results, together with the time stamp, are then saved in the database.

2025

In today's world several image manipulation software's are available. Manipulation of digital images has become a serious problem nowadays. There are many areas like medical imaging, digital forensics, journalism, scientific publications, etc, where image forgery can be done very easily. 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. The detection methods can be very useful in image forensics which can be used as a proof for the authenticity of a digital image. In this paper we propose the method to detect region duplication forgery by dividing the image into overlapping block and then perform searching to find out the duplicated region in the image.

2025

Philippe Schiesser, Le plomb, métal souvent oublié de la numismatique… Mais ni des faussaires, ni de la fabrication monétaire, Money, it’s a hit, Mélanges sonnants et trébuchants offerts à Jean-Marc Doyen, édité par Pierre Cattelain,... more

2025, Positif Journal

The development of picture editing software
over the past several years has led to the
establishment of a topic of active research in
the field of digital image fraud detection.
Passive forgery detection, or Copy Move
Forgery Detection (CMFD), is the focus of
this work. Using the copy move approach, it
is applied to photographs that have been
altered. The proposed feature extraction
method for a CMFD technique that uses 2
Nearest Neighbor (2NN) with Hierarchical
Agglomerative Clustering (HAC) as the
feature matching method is Oriented
Features from Accelerated Segment Test
and rotated Binary Robust Independent
Elementary Features (Oriented FAST and
rotated BRIEF). It is suggested that this
method be used for CMFD. The suggested
CMFD method was tested on pictures that
were subjected to different geometrical
attacks at different times. The suggested
approach for evaluations, which utilizes
images from the MICC-F600 and MICC
F2000 databases, can achieve an overall
accuracy rate of 84.33% and 82.79%,
respectively. When applied to photos that
had been manipulated in a number of ways,
such as rotated, magnified, or object
translated, the True Positive Rate for forgery
detection was above 91%.

2024, Journal of Applied Informatics and Computing (JAIC)

In the digital age, image manipulation is common, often done before publication on social media. However, this can lead to negative impacts, including visual deception. This research aims to detect splicing type image manipulation using Dyadic Wavelet Transform (DyWT) and Scale Invariant Feature Transform (SIFT) methods. The process starts with image decomposition using DyWT to obtain LL sub-images, followed by local feature extraction using SIFT. An application built on desktop-based Matlab source was developed to detect splicing forgery in digital images. The test used 20 images, this image dataset was taken from canon 5d mark II camera and Vivo X80 mobile phone. Each 10 original images, and 10 edited images. These 10 original images are left as they are without making changes, editing or manipulation, while the other 10 images are changed, edited or manipulated using editing software, the results of this editing are uploaded to social media, such as Facebook and Instagram, which will later be used as datasets in testing. The results show that the splicing technique is detected accurately, and processing is faster on images with low pixel resolution. The DyWT and SIFT methods are effective in detecting post-processing attacks such as rotation and rescaling, although they have drawbacks. DyWT struggles in detecting subtle changes and noise, while SIFT is less effective on non-geometric manipulations. Overall, both methods face challenges in detecting complex manipulations and require significant computational resources, especially on high-resolution images.

2024

German and English Die sogenannte Anubismaske des Roemer- und Pelizaeus-Museums Hildesheim (Inv. PM 1585) wurde 1910 von Wilhelm Pelizaeus im ägyptischen Antikenhandel erworben und 1911 im Rahmen einer Schenkung der Stadt Hildesheim... more

2024, Lecture Notes in Computer Science

2024, ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

The increase in the number of vehicles and the alarming rate of theft and defaulters daily prompts the need for sophisticated matching technology to curb car theft, reduce traffic offenders, and any other anomalies/irregularities affecting vehicles' smooth operation. This study deals with the design of an automatic license plate reader which automatically captures an image of the vehicle's license plate, transforms that image into alphanumeric characters using optical character recognition or similar high-tech software, and compares the plate number acquired to one or more databases of vehicles of interest to law enforcement and other agencies against those of stolen cars or people suspected of being involved in criminal activities. The automated capture, analysis, and comparison of vehicle license plates typically occur within seconds enabling the officer in charge to take appropriate actions.

2024

The technique of video copy-move forgery (CMF) is commonly employed in various industries; digital videography is regularly used as the foundation for vital graphic evidence that may be modified using the aforementioned method. Recently in the past few decades, forgery in digital images is detected via machine intellect. The second issue includes continuous allocation of parallel frames having relevant backgrounds erroneously results in false implications, detected as CMF regions third include as the CMF is divided into inter-frame or intra-frame forgeries to detect video copy is not possible by most of the existing methods. Thus, this research presents the dual deep network (DDN) for efficient and effective video copy-move forgery detection (VCMFD); DDN comprises two networks; the first detection network (DetNet1) extracts the general deep features and second detection network (DetNet2) extracts the custom deep features; both the network are interconnected as the output of DetNet1 is given to DetNet2. Furthermore, a novel algorithm is introduced for forged frame detection and optimization of the falsely detected frame. DDN is evaluated considering the two benchmark datasets REWIND and video tampering dataset (VTD) considering different metrics; furthermore, evaluation is carried through comparing the recent existing model. DDN outperforms the existing model in terms of various metrics.

2024, International Journal of Digital Crime and Forensics

Thisarticlepresentsanalgorithmforreadingbothsingleandmultipledigitalvideoclocksbyusinga context-awarepixelperiodicitymethodandadeeplearningtechnique.Readingdigitalvideoclocks... more

Thisarticlepresentsanalgorithmforreadingbothsingleandmultipledigitalvideoclocksbyusinga context-awarepixelperiodicitymethodandadeeplearningtechnique.Readingdigitalvideoclocks inrealtimeisaverychallengingproblem.Thefirstchallengeistheclockdigitlocalization.The existingpixelperiodicityisnotapplicabletolocalizingmultiplesecond-digitplaces.Thisarticle proposesacontext-awarepixelperiodicitymethodtoidentifythesecond-pixelsofeachclock.The secondchallengeisclock-digitrecognition.Forthistask,thealgorithmsbasedadomainknowledge anddeeplearningtechniqueisproposedtorecognizeclockdigits.Theproposedalgorithmisbetter thantheexistingbestoneintwoaspects.Thefirstoneisthatitcanreadnotonlysingledigitvideo clockbutalsomultipledigitvideoclocks.Theotheristhatitrequiresashortlengthofavideoclip. Theexperimentalresultsshowthattheproposedalgorithmcanachieve100%ofaccuracyinboth localizationandrecognitionforbothsingleandmultipleclocks.

2024, International Journal of Digital Crime and Forensics

This article presents an algorithm for reading both single and multiple digital video clocks by using a context-aware pixel periodicity method and a deep learning technique. Reading digital video clocks in real time is a very challenging problem. The first challenge is the clock digit localization. The existing pixel periodicity is not applicable to localizing multiple second-digit places. This article proposes a context-aware pixel periodicity method to identify the second-pixels of each clock. The second challenge is clock-digit recognition. For this task, the algorithms based a domain knowledge and deep learning technique is proposed to recognize clock digits. The proposed algorithm is better than the existing best one in two aspects. The first one is that it can read not only single digit video clock but also multiple digit video clocks. The other is that it requires a short length of a video clip. The experimental results show that the proposed algorithm can achieve 100% of acc...

2024, Dutse Journal of Pure and Applied Sciences

Surveillance videos provide security and increases work efficiency in places of work and homes. as the most acceptable form of evidence, surveillance videos are now tampered to hide actions or convey wrong information. Researchers have proposed ways to mitigate the effect of activities of the attackers through checking the authenticity of the video. The proposed schemes suffer performance degradation in the presence of scene changes. Recently a scheme that addresses the effects of scene change on inter-frame forgery detection was developed where it detects scene changes and divides multiple scenes in to shots. The scheme improves the overall performance of the inter-frame forgery detection at the expense of high average computational time. In this research, a video scene change aware forgery detection scheme is proposed to mitigate the effect of scene change on inter-frame forgery detection with low average computational time. The proposed scheme utilizes the luminance level within frame region which is a more efficient feature to detect scene change. The experimental results show that the scheme has 57% decreases in computational average time and increased in accuracy to 99.03%.

2024

Recent advancements have witnessed the emergence of a software tool that utilizes machine learning techniques to generate audio deepfakes with remarkable realism. These audio manipulations involve recreating voices making it increasingly difficult to distinguish between fabricated content. Commonly known as " deepfakes " these manipulations present risks, including the dissemination of misinformation the creation of fraudulent voice recordings and potential engagement, in malicious activities.To address this escalating threat a groundbreaking study introduces a method specifically designed to identify audio deepfakes with a particular focus on temporal considerations. The proposed approach employs a structured framework that incorporates techniques, for detecting manipulated audio content. Within this system a deep neural network is employed to extract features at the audio frame level.The extracted characteristics are then utilized for training a network (RNN). This empowers the RNN to distinguish between clips that have been manipulated and those that are authentic.To validate the effectiveness of this approach a thorough evaluation is conducted using a dataset of audio deepfakes gathered from different sources. The research demonstrates the systems capability to generate outcomes in detecting audio content all while employing a simple and efficient architectural design.

2024

Steganography is a technology in ‘information hiding’ that allows us to hide a
message (text, document, image, audio, video, etc.) in an image or video.LSB technique is very
simple and popular data hiding method in which cover image holds on bit secret data in each
pixel. So no of data bit in cover image depends on no of pixels in cover image. In this study we
are going to discuss that the capacity of the cover image can be increased via incorporating two
mathematical tools in it. One is compressing secret message using DCT compression and second
using Seam Carving for increasing the resolution of the cover image.

2024, Nucleation and Atmospheric Aerosols

Using survival function and transmuted formula to produce lifetime models with application on real data set

2024, ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

2024

Copy-move is one of the most common image tampering method. Many schemes have been proposed to detect and locate the forged regions. However, many existing schemes fail when the copied region is rotated or flipped before being pasted. To solve the problem, this paper presents a new method for detecting the copy-move forgery. The image is first filtered and divided into overlapping circular blocks. Then the features of the circular blocks are extracted using rotation invariant uniform local binary patterns (LBP). The feature vectors are then compared and the forged regions can be located by tracking the corresponding blocks. Experimental results demonstrate that this method is robust not only to JPEG compression, noise contamination and blurring, but also to region rotation and flipping.

2024, Computers, Materials & Continua

This paper is concerned with a vital topic in image processing: color image forgery detection. The development of computing capabilities has led to a breakthrough in hacking and forgery attacks on signal, image, and data communicated over networks. Hence, there is an urgent need for developing efficient image forgery detection algorithms. Two main types of forgery are considered in this paper: splicing and copy-move. Splicing is performed by inserting a part of an image into another image. On the other hand, copy-move forgery is performed by copying a part of the image into another position in the same image. The proposed approach for splicing detection is based on the assumption that illumination between the original and tampered images is different. To detect the difference between the original and tampered images, the homomorphic transform separates the illumination component from the reflectance component. The illumination histogram derivative is used for detecting the difference in illumination, and hence forgery detection is accomplished. Prior to performing the forgery detection process, some pre-processing techniques, including histogram equalization, histogram matching, high-pass filtering, homomorphic enhancement, and single image super-resolution, are introduced to reinforce the details and changes between the original and embedded sections. The proposed approach for copy-move forgery detection is performed with the Speeded Up Robust Features (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matching with Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogram equalization and single-mage super-resolution. Simulation results proved the feasibility and the robustness of the pre-processing step in homomorphic detection and SURF detection algorithms for splicing and copy-move forgery detection, respectively.

2024, IET Image Processing

With the massive increase of online content, widespread of social media, the popularity of smartphones, and rise of security breaches, image forensics has attracted a lot of attention in the past two decades alongside the advancements in digital imaging and processing software. The goal is to be able to verify authenticity, ownership, and copyright of an image and detect changes to the original image. However, more sophisticated image manipulation software tools can use subtle anti-forensics techniques (AFTs) to complicate and hinder detection. This leads security professionals and digital investigators to develop more robust forensics tools and counter solutions to defeat adversarial anti-forensics and win the race. This survey study presents a comprehensive systematic overview of various anti-forensics and anti-AFTs that are proposed in the literature for digital image forensics. These techniques are thoroughly analysed based on various important characteristics and grouped into broad categories. This study also presents a bibliographic analysis of the-state-of-the-art publications in various venues. It assists junior researchers in multimedia security and related fields to understand the significance of existing techniques, research trends, and future directions.

2024, International Journal of Advanced Research in Computer Science

Nowadays photo manipulation made easier to play with the image files even by a layman. The Combining certain elements to create the unique image that can convince even the most experienced set of eyes. Time to time various detection techniques are developed to identify the image tampering operation over images. In this paper, first, various methods of tampering the image are discussed and the various detection techniques are surveyed. Finally, concluded the comparative study with some parameters.

2024

Abstract: Automatic number plate recognition (ANPR) is a picture processing technology which uses a number (license) plate to spot the vehicle. The main objective is to efficiently design an automatic vehicle identification system by using the vehicle's number plate. The system is implemented in traffic rules and regulation, Parking Management etc. It can be also used in on the entrance for security control of a highly restricted area like military zones or areas around top government offices e.g. Military Base, Parliament, Supreme Court etc. The developed system initially detects the vehicle then captures the vehicle image. Vehicle number plate region is captured using the image segmentation in a picture. CNN is used to improve the plate detection The resulting data is then can be used to compare with the records on a database so as to come up with the specific information like the vehicle owner, place of registration, address, etc. The system is implemented using Python and Op...

2024, IOSR Journal of Computer Engineering

Detection of image forgery is always a crucial factor in image forensic and security applications. Usually this detection is possible with the help of local or global features of an image. We can ensure the credibility of an image with a hashing method by fusing local and global features together. So that it is possible to detect even sensitive image forgeries. Here, we are proposing an improved hashing method for the detection of Copy-move forgery detection and Spliced Image Detection.

2024, Journal of medical signals and sensors

Background: Cancer is a complex disease which can engages the immune system of the patient. In this regard, determination of distinct immunosignatures for various cancers has received increasing interest recently. However, prediction accuracy and reproducibility of the computational methods are limited. In this article, we introduce a robust method for predicting eight types of cancers including astrocytoma, breast cancer, multiple myeloma, lung cancer, oligodendroglia, ovarian cancer, advanced pancreatic cancer, and Ewing sarcoma. Methods: In the proposed scheme, at first, the database is normalized with a dictionary of normalization methods that are combined with particle swarm optimization (PSO) for selecting the best normalization method for each feature. Then, statistical feature selection methods are used to separate discriminative features and they were further improved by PSO with appropriate weights as the inputs of the classification system. Finally, the support vector machines, decision tree, and multilayer perceptron neural network were used as classifiers. Results: The performance of the hybrid predictor was assessed using the holdout method. According to this method, the minimum sensitivity, specificity, precision, and accuracy of the proposed algorithm were 92.4 ± 1.1, 99.1 ± 1.1, 90.6 ± 2.1, and 98.3 ± 1.0, respectively, among the three types of classification that are used in our algorithm. Conclusion: The proposed algorithm considers all the circumstances and works with each feature in its special way. Thus, the proposed algorithm can be used as a promising framework for cancer prediction with immunosignature.

2024, Advances in Electrical and Electronic Engineering

The evolution of modern cameras, mobile phones equipped with sophisticated image editing software has revolutionized digital imaging. In the process of image editing, contrast enhancement is a very common technique to hide visual traces of tampering. In our work, we have employed statistical distribution of block variance and AC DCT coefficients of an image to detect global contrast enhancement in an image. The variation in statistical parameters of block variance and AC DCT coefficients distribution for different degrees of contrast enhancement are used as features to detect contrast enhancement. An SVM classifier with 10 − f old cross-validation is employed. An overall accuracy greater than 99 % in detection with false rate less than 2 % has been achieved. The proposed method is novel and it can be applied to uncompressed, previously JPEG compressed and post enhancement JPEG compressed images with high accuracy. The proposed method does not employ oft-repeated image histogrambased approach.

2024, Security and Communication Networks

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms...

2024, International Journal for Research in Applied Science and Engineering Technology

This project proposes an efficient realization of deep learning, which monitors the conspicuous behaviour of animals in disaster-prone areas and alerts the authorities just in case of any uncertainty. This project entails monitoring animal movement and the use of Convolution Neural Network(CNN), Spectrograms and Mel-frequency cepstral coefficients (MFCC) within the development of animal sound activity detection which is an essential part within the development of earthquake and natural disaster prediction using unusual animal behavior. CNNs are efficient for image classification. Since Spectrograms are 2D inputs that are like images, it is possible that a number of these techniques can transfer over to audio classification.

2024, Advanced Concepts for Intelligent Vision Systems

In this article, we propose a novel approach for discerning which scanner has been used to scan a particular document. Its originality relates to a signature extracted in the wavelet domain of the digitized documents where the acquisition noise specific to a scanner is located in the first subbands of details. This signature is an estimate of the statistical noise model which is modeled by a General Gaussian distribution (GGD) and whose parameters are estimated in the HH subband by maximizing the likelihood function. These parameters constitute a unique identifier for a scanner. For a given image, we propose to identify its origin by minimizing the Kullback-Leibler divergence between its signature and those of known scanners. Experiments conducted on a real scanned-image database, developed for the validation of the work presented in this paper, show that the proposed approach achieves high detection performance. Total of 1000 images were used in experiments.

2024, Data

Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured dataset...

2024, Journal of Computer Science and Technology

Video-based fire detection (VFD) technologies have received significant attention from both academic and industrial communities recently. However, existing VFD approaches are still susceptible to false alarms due to changes in illumination, camera noise, variability of shape, motion, colour, irregular patterns of smoke and flames, modelling and training inaccuracies. Hence, this work aimed at developing a VSD system that will have a high detection rate, low false-alarm rate and short response time. Moving blocks in video frames were segmented and analysed in HSI colour space, and wavelet energy analysis of the smoke candidate blocks was performed. In addition, Dynamic texture descriptors were obtained using Weber Local Descriptor in Three Orthogonal Planes (WLD-TOP). These features were combined and used as inputs to Support Vector Classifier with radial based kernel function, while post-processing stage employs temporal image filtering to reduce false alarm. The algorithm was imple...

2024

In today’s digital world because of the widespread availability of software tools and advancement in technology created a way for editing or modifying digital data. Availability of low cost software tools such as Adobe Premiere, Magix Vegas, Mokey by Imagineer Systems and Microsoft Movie Maker made it very easy to manipulate video content effectively. Altering the contents of digital videos became very easy because of these powerful video editing software. Video editing software is often used to copy and paste a portion of the frame from one region to another region in the same frame or another frame, in order to conceal the truth. Region duplication is one of the most common methods of video tampering. Many methodologies have been discussed by different researches to detect such tampering in digital video sequences by analyzing the spatial and temporal correlations between the pixels of the frame. However, most of the algorithms exhibit low accuracy and high computational complexit...

2023, Computers, Materials & Continua

2023

Now a day the automation of technology Video processing tools and techniques are available for altering the Videos for forgery. The modification or changes in current Video is vital to detect since this Video can be used in the authentication process. Therefore the credibility of video must be verified and it is done with the help of forgery detection mechanism. The various video Tampering ways are resampling, copy and move, splicing etc. In this paper techniques used to detect forgery from within the Video are analyzed. The technique analyzed in this literature includes i) inter frame forgery detection ii) intra frame forgery detection iii) object based mechanism iv) pixel based approach Examination of different techniques and expressing best possible mechanism for forgery detection is aim of this literature. Object based and pixel based approaches are of prime concern in most of research work. The object-based approaches uses applications of abstraction and provide different appro...

2023, ACM Transactions on Multimedia Computing, Communications, and Applications

The outbreak of digital devices on the Internet, the exponential diffusion of data (images, video, audio, and text), along with their manipulation/generation also by Artificial Intelligence (AI) models, e.g., Generative Adversarial Networks (GANs), have created a great deal of concern in the field of forensics. A malicious use can affect relevant application domains, which often include counterfeiting biomedical images, and deceiving biometric authentication systems, as well as their use in scientific publications, in the political world, and even in school activities. It has been demonstrated that manipulated pictures most likely represent indications of malicious behavior, such as photos of minors to promote child prostitution or false political statements. Following this widespread behavior, various forensic techniques have been proposed in the scientific literature over time both to defeat these spoofing attacks as well as to guarantee the integrity of the information. Focusing ...

2023, Electronics

Frame duplication forgery is the most common inter-frame video forgery type to alter the contents of digital video sequences. It can be used for removing or duplicating some events within the same video sequences. Most of the existing frame duplication forgery detection methods fail to detect highly similar frames in the surveillance videos. In this paper, we propose a frame duplication forgery detection method based on textural feature analysis of video frames for digital video sequences. Firstly, we compute the single-level 2-D wavelet decomposition for each frame in the forged video sequences. Secondly, textural features of each frame are extracted using the Gray Level of the Co-Occurrence Matrix (GLCM). Four second-order statistical descriptors, Contrast, Correlation, Energy, and Homogeneity, are computed for the extracted textural features of GLCM. Furthermore, we calculate four statistical features from each frame (standard deviation, entropy, Root-Mean-Square RMS, and varianc...

2023, IEEE Access

These days, videos can be easily recorded, altered and shared on social and electronic media for deception and false propaganda. However, due to sophisticated nature of the content alteration tools, alterations remain inconspicuous to the naked eye and it is a challenging task to differentiate between authentic and tampered videos. During the process of video tampering the traces of objects, which are removed or modified, remain in the frames of a video. Based on this observation, in this study, a new method is introduced for discriminating authentic and tampered video clips. This method is based on deep model, which consists of three types of layers: motion residual (MR), convolutional neural network (CNN), and parasitic layers. The MR layer highlights the tampering traces by aggregation of frames. The CNN layers encode these tampering traces and are learned using transfer learning. Finally, parasitic layers classify the video clip (VC) as authentic or tampered. The parasitic layers are learned using an efficient learning method based on extreme learning theory; they enhance the performance in terms of efficiency and accuracy. Intensive experiments were performed on various benchmark datasets to validate the performance and the robustness of the method; it achieved 98.89% accuracy. Comparative analysis shows that the proposed method outperforms the state-of-the-art methods.