Tampering Methods Research Articles

UFCC: A Unified Forensic Approach to Locating Tampered Areas in Still Images and Detecting Deepfake Videos by Evaluating Content Consistency

Image inpainting and Deepfake techniques have the potential to drastically alter the meaning of visual content, posing a serious threat to the integrity of both images and videos. Addressing this challenge requires the development of effective methods to verify the authenticity of investigated visual data. This research introduces UFCC (Unified Forensic Scheme by Content Consistency), a novel forensic approach based on deep learning. UFCC can identify tampered areas in images and detect Deepfake videos by examining content consistency, assuming that manipulations can create dissimilarity between tampered and intact portions of visual data. The term “Unified” signifies that the same methodology is applicable to both still images and videos. Recognizing the challenge of collecting a diverse dataset for supervised learning due to various tampering methods, we overcome this limitation by incorporating information from original or unaltered content in the training process rather than relying solely on tampered data. A neural network for feature extraction is trained to classify imagery patches, and a Siamese network measures the similarity between pairs of patches. For still images, tampered areas are identified as patches that deviate from the majority of the investigated image. In the case of Deepfake video detection, the proposed scheme involves locating facial regions and determining authenticity by comparing facial region similarity across consecutive frames. Extensive testing is conducted on publicly available image forensic datasets and Deepfake datasets with various manipulation operations. The experimental results highlight the superior accuracy and stability of the UFCC scheme compared to existing methods.

DS‐Net: Dual supervision neural network for image manipulation localization

AbstractWith the rapid development of image editing technology, tampering with images has become easier. Maliciously tampered images lead to serious security problems (e.g., when used as evidence). The current mainstream methods of image tampering are divided into three types which are copy‐move, splicing and removal. Many image tampering detection methods can only detect one type of image tampering. Additionally, some methods learn features by suppressing image content, which can result in false positives when identifying tampered areas. In this paper, the authors propose a novel framework named the dual supervision neural network (DS‐Net) to localize the tampered regions of images tampered by the three tampering methods mentioned above. First, to extract richer multiscale information, the authors add skip connections to the atrous spatial pyramid pooling (ASPP) module. Second, a channel attention mechanism is introduced to dynamically weigh the results generated by ASPP. Finally, the authors build additional supervised branches for high‐level features to further enhance the extraction of these high‐level features before fusing them with low‐level features. The authors conduct experiments on various standard datasets. Through extensive experiments, the results show that the AUC scores reach 86.4%, 95.3% and 99.6% for CASIA, COVERAGE and NIST16 datasets, respectively, and the F1 scores are 56.0%, 73.4% and 82.7%, respectively. The results demonstrate that the authors’ method can accurately locate tampered regions and achieve better performance on various datasets than other methods of the same type.

MRT-Net: Auto-adaptive weighting of manipulation residuals and texture clues for face manipulation detection

With the growing popularity of social media and an outburst of malicious tampering methods, it has become easier than ever to mislead others and create disharmony in society by manipulating social media content. Hence, there is an urgent need to build robust manipulation detection systems. This manuscript proposes a novel end-to-end architecture that extracts discriminative “manipulation residuals” (MR) and “textural” (T) features for facial manipulation detection. Most multi-branch architectures suffer from a common flaw in their fusion stage, where they combine multi-domain features in equal proportion. This is disadvantageous since each feature may not be equally important in making the final prediction. MRT-Net solves this problem by implementing an auto-adaptive weighting mechanism to select an ideal proportion of manipulation residual and textural information that prove complementary to one another. Specifically, two weighting factors for the MR and T features, ∝1 and ∝2 are added as parameters to the proposed neural network and are updated automatically via backpropagation, thereby allowing MRT-Net to find the ideal mix of residual and textural information. Additionally, MRT-Net benefits from a channel attention mechanism that boosts its performance even further. MRT-Net achieves excellent performance on three public benchmark datasets, Deep Fake Detection Challenge (DFDC), CelebDF and FaceForensics++ (FF++). AUC scores achieved are 0.9964 on DFDC, 0.9921 on CelebDF, 0.9910 on FF++(DeepFake), 0.9974 on FF++(Face2Face), 0.9942 on FF++(FaceShifter), 0.9933 on FF++(FaceSwap) and 0.9662 on FF++(NeuralTextures). It also achieves accuracy scores of 0.9760 on DFDC, 0.9815 on CelebDF, 0.9670 on FF++(DeepFake), 0.9767 on FF++(Face2Face), 0.9611 on FF++(FaceShifter), 0.9676 on FF++(FaceSwap) and 0.9025 on FF++(NeuralTextures). These excellent results demonstrate MRT-Net's potency as it comfortably outperforms other state-of-the-art face manipulation detection methods.

Analysis on Feasible Methods of Digital Image Tampering Detection

In order to solve the problem of image tampering detection in the blind forensics of digital images, this study first researches and summarizes the means and methods of image tampering and forgery, and provides suggestions for methods of tampering detection using open source tools for the methods that may be used for falsification. These methods have been publicly tested by open source companies for a long time, which proves their feasibility. The methods and conclusions suggested by the research can be used in the "falsification" application of pictures in many fields, and have certain academic value and social value.

Characterizing the Experience of Tapentadol Nonmedical Use: Mixed Methods Study

BackgroundThe prevalence of abuse, diversion, and web-based endorsement of tapentadol (extended-release [ER], immediate-release [IR]) has been characterized as low compared with other prescription opioids. Little is known about individual experience with tapentadol nonmedical use (NMU).ObjectiveThis study aims to pilot web-based survey technologies to investigate the motivation for tapentadol NMU, sources of procurement, routes of administration, tampering methods, doses used, and impressions of tapentadol products (Nucynta and Nucynta ER).MethodsRecruitment flyers and banner advertisements were placed on the Bluelight website [DragonByte Technologies Ltd] with a link to a web-based survey (Qualtrics) designed to query about individuals’ lifetime tapentadol NMU. This web-based survey was followed by an interactive web-based chat (Cryptocat) with respondents who were willing to be contacted. Respondents were queried about sources for obtaining tapentadol, motives for use, routes of administration, tampering methods, drugs used in combination, tablet strengths and dosages, and reasons for continued or discontinued use. Desirability and attractiveness for NMU was rated.ResultsWeb-based recruitment successfully attracted difficult-to-find study participants. A total of 78 participants reported that tapentadol was obtained from friends and family (ER 11/30, 37%; IR 18/67, 27%), the internet (ER 11/30, 37%; IR 12/67, 18%) or participants’ own prescriptions from a doctor (ER 9/30, 30%; IR 17/67, 25%). It was used nonmedically for pain relief (ER 18/30, 60%; IR 33/67, 49%) and multiple psychotropic effects, including relaxation (ER 13/30, 43%; IR 29/67, 43%), reduction in depression or anxiety (ER 7/30, 23%; IR 30/67, 45%), or getting high (ER 12/30, 40%; IR 33/67, 49%). Tapentadol was primarily swallowed (ER 22/30, 73%; IR 55/67, 82%), although snorting (ER 2/30, 7%; IR 8/67, 12%) and injection (ER 2/30, 7%; IR 5/67, 8%) were also reported. The preferred dose for NMU was 100 mg (both ER and IR). The participants reported tapentadol use with benzodiazepines (ER 12/21, 57%; IR 28/47, 60%). Most participants had discontinued tapentadol NMU at the time of survey completion (ER 22/30, 73%; IR 55/67, 82%). Reasons for discontinued ER NMU included side effects (10/22, 46%) and lack of effective treatment (10/22, 46%). Reasons for discontinued IR NMU included lack of access (26/55, 47%) and better NMU options (IR 21/55, 38%). Few individuals were willing to divulge identifying information about themselves for the interactive chat (8/78, 10%), demonstrating the strength of anonymous, web-based surveys. Interactive chat supported the survey findings. A subgroup of participants (4/78, 5%) reported hallucinogenic side effects with high doses.ConclusionsWeb-based surveys can successfully recruit individuals who report drug NMU and those who are difficult to find. Tapentadol NMU appears to occur primarily for pain relief and for its psychotropic effects. Although it was liked by some, tapentadol did not receive a robust pattern of endorsement for NMU.

Analisis Digital Forensik Keaslian Video Rekaman CCTV Menggunakan Metode Localization Tampering

Video is one the valid evidence if the handling process is in accordance with digital forensic procedures. Closed circuit television is a video sourse that is often used as authentic evidence in court. The authenticity of the video is something that is often doubted by certain parties. For this reason, this discusses how to detect the authenticity of video as digital evidence by comparing the original video files and tampering video files resulting from attack frame addition and attack frame delection. The media info tool is used to analyze the metadata and the localization tampering method is used to detect the frame where manipulation occurs. Localization tampering analyzes frame by frame, calculates as histogram and displays a histogram graph. Based on the results of the metadata analysis of the original video file and the video file tampering, it displays different information, which means that the video has been manipulated. Next, analyze the video with the localization tampering method to display the location on the video frame where manipulation has occurred. From the analysis results provide different information both from the calculation of the RGB value and the histogram graph.

A New Model for Bias-Generating Agent-Based Simulation and Its Application to Election Systems: Allowing Agents to Make Mistakes for a Reason

Several studies have proposed that vote tampering based on heuristic algorithms can manipulate voters’ votes. It can be found from the analysis of the poll results of the 2016 US election that the frequency of “Trump won,” which is generally considered a black swan phenomenon, is not low and even reached 16.8%. However, many models are unable to restore the generation of such a high frequency of black swan phenomena. In this study, the black swan phenomenon is successfully reproduced using a bias-generating agent-based election system model. By adjusting the tampering method, the frequency of the black swan phenomenon will change from 5% to 15%. From the simulation results, it can be observed that one of the possible causes of the black swan phenomenon is the tampering of the voting results, which leads to more biased voters, thus increasing the frequency of the winning elections. This study proposes that to obtain more realistic simulation results, it is necessary to introduce more realistic perceptual models for agents, rather than relying solely on random functions. Allowing agents to make mistakes for a reason should be an integral part of multi-agent-based simulation in the field of pairwise human simulation.

A two-stage detection method of copy-move forgery based on parallel feature fusion

The copy-move forgery refers to the copying and pasting of a region of the original image into the target region of the same image, which represents a typical tampering method with the characteristics of easy tampering and high-quality tampering. The existing single feature-based methods of forgery detection have certain shortcomings, such as high false alarm rate, low robustness, and low detection accuracy. To address these shortcomings, this paper proposes an improved two-stage detection method based on parallel feature fusion and an adaptive threshold generation algorithm. Firstly, the SLIC super-pixels segmentation algorithm is used for image preprocessing, and a similar region extraction algorithm without threshold is employed to obtain the suspected tampering regions with high similarity. Secondly, the parallel fusion feature is obtained based on the SIFT and HU features to express the characteristics of local regions. Then, the corresponding threshold value is generated based on the histogram of oriented gradient (HOG) to describe the texture characteristics of the obtained regions, which acts as a criterion to judge whether a region has been forged or not. The experimental results show that the proposed method outperforms the existing methods, achieving the accuracy of 99.01% and 98.5% on the MICC-F220 and MICC-F2000 datasets respectively. In addition, the proposed method has stronger robustness performance on COMOFOD dataset than the comparison methods.

ESRNet: Efficient Search and Recognition Network for Image Manipulation Detection

With the widespread use of smartphones and the rise of intelligent software, we can manipulate captured photos anytime and anywhere, so the fake photos finally obtained look “Real.” If these intelligent operation methods are maliciously applied to our daily life, then fake news, fake photos, rumors, slander, fraud, threats, and other information security issues around us can happen all the time. Today’s intelligent retouching software can make various modifications to photos, some of which do not change the content that the photos themselves want to express, such as retouching, contrast improvement, and so on. In this article, we mainly study the three operation modes of changing the authenticity of photo contents, which are Copy-move, Splicing, and Removal. Few scholars have done relevant research due to the lack of a corresponding dataset. To address this issue, we elaborately collect a novel dataset, called the multi-realistic scene manipulation dataset ( MSM30K ), which consists of 30,000 images, including three types of tampering methods, and covering 32 different tampering scenes in life. In addition, we propose a unified detection network: the efficient search and recognition network ( ESRNet ) for three tampering methods. It mainly includes four main modules: Efficient feature pyramid network ( EFPN ), Residual receptive field block with attention ( RFBA ), Hierarchical decoding identification ( HDI ), and Cascaded group-reversal attention ( GRA ) blocks. On these three datasets, ESRNet can reach 0.81 on the S-measure, 0.72 on the F-measure, and 0.85 on the E-measure. The inference speed is ~53 fps on a single GPU without I/O time. ESRNet outperforms various state-of-the-art manipulation detection baselines on three image manipulation datasets.

Radar-based monitoring system for medication tampering using data augmentation and multivariate time series classification

Inadvertent use of medication that has been tampered with can cause serious harm. Monitoring how and when medication was last used or touched is important for mitigating risks. In this paper, we present a new radar-based monitoring system that can detect eight different types of tampering methods with three types of medication containers. Our system works by using a FMCW and CW Doppler radar to capture motion speed, direction, and range, which we use for classifying activities. For monitoring activities at home, our system can be set up underneath a kitchen cabinet to monitor medication left out on the countertop. As our system uses radar, we can preserve privacy of individuals as the signatures from the radar are specific to the locations of the antennas and not necessarily associated with an individual. For classifying activities we created a processing pipeline that extracts a set of features from the raw multivariate time series signals from the radar. We then used three types of data augmentation techniques including jittering, scaling, and magnitude warping, to increase our data sets and increase our classification model accuracy. In addition, we evaluated our system using 5-fold cross validation and with different types of augmentation data sets. Our system can achieve 99% accuracy using a logistic regression classifier with multiple people.

A Survey of Anti-forensic for Face Image Forgery

Deep learning related technologies, especially generative adversarial network, are widely used in the fields of face image tampering and forgery. Forensics researchers have proposed a variety of passive forensic and related anti-forensic methods for image tampering and forgery, especially face images, but there is still a lack of overview of anti-forensic methods at this stage. Therefore, this paper will systematically discuss the anti-forensic methods for face image tampering and forgery. Firstly, this paper expounds the relevant background, including the relevant tampering and forgery methods and forensic schemes of face images. The former mainly includes four aspects: conventional processing, fake face generation, face editing and face swapping; The latter is mainly the relevant forensic means based on spatial domain and frequency domain using deep learning technology. Then, this paper divides the existing anti-forensic works into three categories according to their method characteristics, namely hiding operation traces, forgery reconstruction and adversarial attack. Finally, this paper summarizes the limitations and prospects of the existing anti-forensic technologies.

Deep Learning Based Image Forgery Detection Methods

Increasingly advanced image processing technology has made digital image editing easier and easier. With image processing software at one’s fingertips, one can easily alter the content of an image, and the altered image is so realistic that it is illegible to the naked eye. These tampered images have posed a serious threat to personal privacy, social order, and national security. Therefore, detecting and locating tampered areas in images has important practical significance, and has become an important research topic in the field of multimedia information security. In recent years, deep learning technology has been widely used in image tampering localization, and the achieved performance has significantly surpassed traditional tampering forensics methods. This paper mainly sorts out the relevant knowledge and latest methods in the field of image tampering detection based on deep learning. According to the two types of tampering detection based on deep learning, the detection tasks of the method are detailed separately, and the problems and future prospects in this field are discussed. It is quite different from the existing work: 1) This paper mainly focuses on the problem of image tampering detection, so it does not elaborate on various forensic methods. 2) This paper focuses on the detection method of image tampering based on deep learning. 3) This paper is driven by the needs of tampering targets, so it pays more attention to sorting out methods for different tampering detection tasks.

A novel pixel’s fractional mean-based image enhancement algorithm for better image splicing detection

Image manipulation has become widely accessible to the masses over the past years due to the sophisticated image editing tools which are readily-available and easy to use. As a result, image forgery has increased such that it has become infeasible to discriminate authentic from tampered images with the naked eye. Image forgery plays a prominent role in the spread of misinformation, which might be criminalized under certain jurisdictions. Image splicing is a common type of image manipulation and constitutes one of the most widespread image tampering methods on the internet. Efforts have been made to tackle the implications of image forgery by developing computer algorithms that can discriminate tampered images, however, more research is needed to keep up with the advancements of image editing tools. Previously, we have explored fractional calculus in other image processing applications. In this study, we propose a novel pixel’s fractional mean (PFM) algorithm to enhance images prior to classification for better detection of image splicing forgery based on texture features. The proposed PFM enhances each pixel separately depending on the occurrence number of the pixel’s intensity. Two sets of texture algorithms are used to extract essential features from suspected spliced images. These features are then used with the “support vector machine” (SVM) classifier to classify authentic and spliced images. The proposed model demonstrated an accuracy rate of 97% when evaluated with the publicly-available image splicing dataset “CASIA v2.0”. With a relatively low dimension feature vector, the proposed model demonstrates high accuracy and efficiency, which corroborate the benefit of using fractional calculus in image processing algorithms.

General Purpose Image Tampering Detection using Convolutional Neural Network and Local Optimal Oriented Pattern (LOOP)

Digital image tampering detection has been an active area of research in recent times due to the ease with which digital image can be modified to convey false or misleading information. To address this problem, several studies have proposed forensics algorithms for digital image tampering detection. While these approaches have shown remarkable improvement, most of them only focused on detecting a specific type of image tampering. The limitation of these approaches is that new forensic method must be designed for each new manipulation approach that is developed. Consequently, there is a need to develop methods capable of detecting multiple tampering operations. In this paper, we proposed a novel general purpose image tampering scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of detecting five types of image tampering in both binary and multiclass scenarios. Unlike the existing deep learning techniques which used constrained pre-processing layers to suppress the effect of image content in order to capture image tampering traces, our method uses LOOP features, which can effectively subdue the effect image content, thus, allowing the proposed CNNs to capture the needed features to distinguish among different types of image tampering. Through a number of detailed experiments, our results demonstrate that the proposed general purpose image tampering method can achieve high detection accuracies in individual and multiclass image tampering detections respectively and a comparative analysis of our results with the existing state of the arts reveals that the proposed model is more robust than most of the exiting methods.

NoPUF: A Novel PUF Design Framework Toward Modeling Attack Resistant PUFs

With the rapid development and globalization of the semiconductor industry, hardware security has emerged as a critical concern. New attacking and tampering methods are continuously challenge current hardware protection methods. Combating these powerful attacks is of great importance in securing hardware devices. This paper proposes a novel framework to protect Physical Unclonable Function (PUF) against modeling attacks, denominated as Noisy PUF (NoPUF). NoPUF exploits structural unpredictability to improve overall security. We present several PUF architectures under the proposed framework that could reconfigure a conventional reliable PUF to a noisy PUF. The reconfigured PUF becomes inherently unreliable and hence achieves a higher resistance against modeling attacks. Moreover, since only a small portion of the Challenge-Response Pairs (CRPs) are required for authentication, the designer can use the information obtained from the initial reliable PUF configuration to find CRPs, which are still reliable in the noisy PUF configuration for authentication. Exploiting such information asymmetry between designer and attacker is the nexus of the proposed NoPUF design methodology. Experimental results show that we can achieve a maximum attacker and designer accuracy difference of 44.79% for a 64-stage NoPUF candidate architecture while ensuring high reliability for selected challenges.

Multiple Fusion Strategies in Localization of Local Deformation Tampering

Tampering with images may involve the field of crime and also bring problems such as incorrect values to the public. Image local deformation is one of the most common image tampering methods, where the original texture features and the correlation between the pixels of an image are changed. Multiple fusion strategies based on first-order difference images and their texture feature is proposed to locate the tamper in local deformation image. Firstly, texture features using overlapping blocks on one color channel are extracted and fed into fuzzy c-means clustering method to generate a tamper probability map (TPM), and then several TPMs with different block sizes are fused in the first fusion. Secondly, different TPMs with different color channels and different texture features are respectively fused in the second and third fusion. The experimental results show that the proposed method can accurately detect the location of the local deformation of an image.

Diffusion-based image inpainting forensics via weighted least squares filtering enhancement

The rapid development of blockchain technology has greatly changed people’s daily lives from financial market to healthcare field. Today, adulterated images appear in large numbers, causing a serious threat to personal privacy and social stability. At present, the research on blockchain is mainly focused on chain data transmission. Though the blockchain can ensure that the chain data transmission is not tampered, it is difficult to ensure that the data is real when placed on the system initially. In addition, the immutability will be destroyed when 51% attacks occur. Taking this point into consideration, it is necessary to identify the image authenticity on the blockchain. Diffusion-based inpainting is a common method of image tampering. Considering the blurring effect introduced by diffusion-based image inpainting, this paper proposes an image forensics method of diffusion-based image inpainting via weighted least squares filtering enhancement. The texture of the forged image is clear in the untouched regions, and the blurring effect leads to some texture changes in the inpainted regions. Weighted least squares filtering can preserve the texture structure of the untouched regions better and highlight the blurring effect of the inpainted regions. In view of the different reflects of the tampered information in different color channels, weighted least squares filtering is applied to enhance each color channel of the input image, which can capture the impact of image inpainting from multiple perspectives. The experimental results show that the proposed method not only makes up for the deficiency of previous blockchain forensics effectively, but also has better detection performance than the existing work.

Urine drug-testing tampering approaches: Turkish probationers

The growing numbers of individual and social problems associated with drug abuse necessitate new approaches in drug-testing systems. Equally, drug abusers may attempt to invalidate drug testing using different methods such as adulteration, dilution and substitution. This study aims to investigate tampering methods commonly used by Turkish substance-using probationers and evaluate their effects on toxicological drug-testing results. Initially, probationer urinary screening test results and laboratory substitution documents were evaluated to investigate the dilution and substitution attempt. Additionally, an experimental study was carried out by using readily available household products (bleach, vinegar, drain opener, eye drops) for adulteration. The effect of these agents was investigated for 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH), amphetamine and 3,4-methylenedioxymethamphetamine (MDMA). It was determined that probationers preferred unbranded products (syringes, nylon bottles, etc.) for urine substitution. To detect dilution, screening test results were evaluated along with creatinine values. The variability of mean creatinine values can change the rate of the before-negative and after-positive ratio. For adulteration method, the high amounts of bleach provided false-negative results for THC-COOH and amphetamine, but spiking in any concentration of bleach affected MDMA results, causing a slight increase. Vinegar did not affect the THC-COOH and amphetamine results. However, false-negative results were observed for MDMA, with high amounts of vinegar-spiked urine samples. Drain opener was added in large quantities, and false-negative results were observed for all analytes. Visine eye drops did not have any effect on THC-COOH or amphetamine, but a high quantity of eye drops had a slight decreasing effect for MDMA.

Electromagnetic compatibility and protection of electric energy meters from strong magnetic fields

Power theft through electric energy meters tampering causes significant revenue losses. One of the meter tampering method involves manipulation using strong magnetic fields to distort the process of energy calculation by saturation of current transformers’ cores. This paper presents an approach towards effective shielding of the power meters’ sensitive elements from external magnetic fields. The theory of magnetic materials and the methods to calculate magnetic fields in the presence of these materials are used to develop a refined protective shield.

A Novel Approach for Copy-move Forgery Detection using Bilateral Filtering

Digital image processing methods have a wide area of usage and their complexity is increasing, as well as the tampering methods. A widely used tampering method is copy-move forgery. In this study, a hybrid method combining the DCT and Bilateral filtering is developed. In this method, first overlapping blocks are obtained from the input image. Then, bilateral filtering and DCT of these blocks are multiplied to obtain the refined block features. The block features are scanned by a zig-zag process followed by a lexicographic sorting. Finally, a similarity detection by a predetermined threshold parameter is applied to detect the forgery. Both visual and quantitative results demonstrated that the proposed method can determine the copy-move forgery regions.