Detecting image manipulation with ELA-CNN integration: a powerful framework for authenticity verification.

The exponential progress of image editing software has contributed to a rapid rise in the production of fake images. Consequently, various techniques and approaches have been developed to detect manipulated images. These methods aim to discern between genuine and altered images, effectively combating the proliferation of deceptive visual content. However, additional advancements are necessary to enhance their accuracy and precision. Therefore, this research proposes an image forgery algorithm that integrates error level analysis (ELA) and a convolutional neural network (CNN) to detect the manipulation. The system primarily focuses on detecting copy-move and splicing forgeries in images. The input image is fed to the ELA algorithm to identify regions within the image that have different compression levels. Afterward, the created ELA images are used as input to train the proposed CNN model. The CNN model is constructed from two consecutive convolution layers, followed by one max pooling layer and two dense layers. Two dropout layers are inserted between the layers to improve model generalization. The experiments are applied to the CASIA 2 dataset, and the simulation results show that the proposed algorithm demonstrates remarkable performance metrics, including a training accuracy of 99.05%, testing accuracy of 94.14%, precision of 94.1%, and recall of 94.07%. Notably, it outperforms state-of-the-art techniques in both accuracy and precision.

Real-time object-removal tampering localization in surveillance videos by employing YOLO-V8.

Videos are considered as most trustworthy means of communication in the present digital era. The advancement in multimedia technology has made video content sharing and manipulation very easy. Hence, the video authenticity is a challenging task for the research community. Video forensics refer to uncovering the forgery traces. The detection of spatiotemporal object-removal forgery in surveillance videos is crucial for judicial forensics, as the presence of objects in the video has significant information as legal evidence. The author proposes a passive max-median averaging motion residual algorithm for revealing the forgery traces, successfully giving visible object-removal traces followed by a deep learning approach, YOLO-V8, for forged region localization. YOLO-V8 is the latest deep learning model, which has a wide scope for real-time application. The proposed method utilizes YOLO-V8 for object-removal forgery in surveillance videos. The network is trained on the SYSU-OBJFORG dataset for object-removal forged region localization in videos. The fine-tuned YOLO-V8 successfully classifies and localizes the object-removal tampered region with an F1-score of 0.99 and a precision of 0.99. The observed high confidence score of the bounding box around the forged region makes the model reliable. This fine-tuned YOLO-V8 would be a better choice in real-time applications as it solves the complex object-based forgery detection in videos. The performance of the proposed system is far better than the existing deep learning approach.

Research on Oracle Technology Based on Multi-Threshold Aggregate Signature Algorithm and Enhanced Trustworthy Oracle Reputation Mechanism.

In the realm of IoT sensor data security, particularly in areas like agricultural product traceability, the challenges of ensuring product origin and quality are paramount. This research presents a novel blockchain oracle solution integrating an enhanced MTAS signature algorithm derived from the Schnorr signature algorithm. The key improvement lies in the automatic adaptation of flexible threshold values based on the current scenario, catering to diverse security and efficiency requirements. Utilizing the continuously increasing block height of the blockchain as a pivotal blinding parameter, our approach strengthens signature verifiability and security. By combining the block height with signature parameters, we devise a distinctive signing scheme reliant on a globally immutable timestamp. Additionally, this study introduces a reliable oracle reputation mechanism for monitoring and assessing oracle node performance, maintaining both local and global reputations. This mechanism leverages smart contracts to evaluate each oracle's historical service, penalizing or removing nodes engaged in inappropriate behaviors. Experimental results highlight the innovative contributions of our approach to enhancing on-chain efficiency and fortifying security during the on-chain process, offering promising advancements for secure and efficient IoT sensor data transmission.

PP-JPEG: A Privacy-Preserving JPEG Image-Tampering Localization.

The widespread availability of digital image-processing software has given rise to various forms of image manipulation and forgery, which can pose a significant challenge in different fields, such as law enforcement, journalism, etc. It can also lead to privacy concerns. We are proposing that a privacy-preserving framework to encrypt images before processing them is vital to maintain the privacy and confidentiality of sensitive images, especially those used for the purpose of investigation. To address these challenges, we propose a novel solution that detects image forgeries while preserving the privacy of the images. Our method proposes a privacy-preserving framework that encrypts the images before processing them, making it difficult for unauthorized individuals to access them. The proposed method utilizes a compression quality analysis in the encrypted domain to detect the presence of forgeries in images by determining if the forged portion (dummy image) has a compression quality different from that of the original image (featured image) in the encrypted domain. This approach effectively localizes the tampered portions of the image, even for small pixel blocks of size 10×10 in the encrypted domain. Furthermore, the method identifies the featured image's JPEG quality using the first minima in the energy graph.

A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends.

Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos. Therefore, it's vital to distinguish between true and fake data. Digital video forgery detection techniques are used to validate and check the realness of digital video content. Deep learning algorithms lately sparked a lot of interest in the field of digital forensics, such as Recurrent Neural Networks (RNN), Deep Convolutional Neural Networks (DCNN), and Adaptive Neural Networks (ANN). In this paper, we give a soft taxonomy as well as a thorough overview of recent research on multimedia falsification detection systems. First, the basic knowledge needed to comprehend video forgery is provided. Then, a summary of active and passive video manipulation detection approaches is provided. Anti-forensics, compression video methods, datasets required for video forensics, and challenges of video detection approaches are also addressed. Following that, we presented an overview of deepfake, and the datasets required for detection were also provided. Also, helpful software packages and forensics tools for video detection are covered. In addition, this paper provides an overview of video analysis tools that are used in video forensic applications. Finally, we highlight research difficulties as well as interesting research avenues. In short, this survey provides detailed information and a broader investigation to extract data and detect fraud video contents under one umbrella.

A forensic evaluation method for DeepFake detection using DCNN-based facial similarity scores.

Detecting DeepFake videos has become a central task in modern multimedia forensics applications. This article presents a method to detect face swapped videos when the portrayed person in the video is known. We propose using a threshold classifier based on similarity scores obtained from a Deep Convolutional Neural Network (DCNN) trained for facial recognition. We compute a set of similarity scores between faces extracted from questioned videos and reference materials of the person depicted. We use the highest score to classify the questioned videos as authentic or fake, depending on the threshold chosen. We validate our method on the Celeb-DF (v2) dataset (Li et al., 2020) [13]. Using the training and testing splits specified on the dataset, we obtained an HTER of 0.020 and an AUC of 0.994, surpassing the most robust approaches against this dataset (Tran et al., 2021) [37]. Additionally, a logistic regression model was used to convert the highest score into a likelihood ratio for greater applicability in forensic analyses.

DCU-Net: a dual-channel U-shaped network for image splicing forgery detection.

The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks.

Fuzzy proximity-based robust data hiding scheme with interval threshold.

Secret communication of sensitive data must progress in a trustworthy environment through data hiding. Using Mamdani fuzzy logic to identify color proximity at the block level and a shared secret key and post-processing system, this paper attempts to develop a robust data hiding scheme with similarity measures to ensure good visual quality, robustness, imperceptibility and enhance the security. In accordance with the Gestalt principle, proximity among the nearby objects is higher, whose value varies from expert to expert. Therefore, a possibility for type-I fuzzy logic to be used to evaluate proximity. Fuzzy proximity is computed by means of a difference in intensity (colordiff) and distance (closeness). Further, the block color proximity obtained from the proximity calculation network is graded using an interval threshold. Accordingly, data embedding is processed in the sequence generated by the shared secret keys. The tampering coincidence problem is solved through a post-processing approach to increase the quality and accuracy of the recovered secret message. The experimental analysis, steganalysis and comparisons clearly illustrate the effectiveness of the proposed scheme in terms of visual quality, structural similarity, recoverability and robustness.

Detection of Double-Compressed Videos Using Descriptors of Video Encoders.

In digital forensics, video becomes important evidence in an accident or a crime. However, video editing programs are easily available in the market, and even non-experts can delete or modify a section of an evidence video that contains adverse evidence. The tampered video is compressed again and stored. Therefore, detecting a double-compressed video is one of the important methods in the field of digital video tampering detection. In this paper, we present a new approach to detecting a double-compressed video using the proposed descriptors of video encoders. The implementation of real-time video encoders is so complex that manufacturers should develop hardware video encoders considering a trade-off between complexity and performance. According to our observation, hardware video encoders practically do not use all possible encoding modes defined in the video coding standard but only a subset of the encoding modes. The proposed method defines this subset of encoding modes as the descriptor of the video encoder. If a video is double-compressed, the descriptor of the double-compressed video is changed to the descriptor of the video encoder used for double-compression. Therefore, the proposed method detects the double-compressed video by checking whether the descriptor of the test video is changed or not. In our experiments, we show descriptors of various H.264 and High-Efficiency Video Coding (HEVC) video encoders and demonstrate that our proposed method successfully detects double-compressed videos in most cases.

Seamless Copy-Move Replication in Digital Images.

The importance and relevance of digital-image forensics has attracted researchers to establish different techniques for creating and detecting forgeries. The core category in passive image forgery is copy-move image forgery that affects the originality of image by applying a different transformation. In this paper, a frequency-domain image-manipulation method is presented. The method exploits the localized nature of discrete wavelet transform (DWT) to attain the region of the host image to be manipulated. Both patch and host image are subjected to DWT at the same level l to obtain 3l+1 sub-bands, and each sub-band of the patch is pasted to the identified region in the corresponding sub-band of the host image. Resulting manipulated host sub-bands are then subjected to inverse DWT to obtain the final manipulated host image. The proposed method shows good resistance against detection by two frequency-domain forgery detection methods from the literature. The purpose of this research work is to create a forgery and highlight the need to produce forgery detection methods that are robust against malicious copy-move forgery.

Physical Tampering Detection Using Single COTS Wi-Fi Endpoint.

This paper proposes a practical physical tampering detection mechanism using inexpensive commercial off-the-shelf (COTS) Wi-Fi endpoint devices with a deep neural network (DNN) on channel state information (CSI) in the Wi-Fi signals. Attributed to the DNN that identifies physical tampering events due to the multi-subcarrier characteristics in CSI, our methodology takes effect using only one COTS Wi-Fi endpoint with a single embedded antenna to detect changes in the relative orientation between the Wi-Fi infrastructure and the endpoint, in contrast to previous sophisticated, proprietary approaches. Preliminary results show that our detectors manage to achieve a 95.89% true positive rate (TPR) with no worse than a 4.12% false positive rate (FPR) in detecting physical tampering events.

Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions.

BACKGROUND: The complex unfolding of the US opioid epidemic in the last 20 years has been the subject of a large body of medical and pharmacological research, and it has sparked a multidisciplinary discussion on how to implement interventions and policies to effectively control its impact on public health. OBJECTIVE: This study leverages Reddit, a social media platform, as the primary data source to investigate the opioid crisis. We aimed to find a large cohort of Reddit users interested in discussing the use of opioids, trace the temporal evolution of their interest, and extensively characterize patterns of the nonmedical consumption of opioids, with a focus on routes of administration and drug tampering. METHODS: We used a semiautomatic information retrieval algorithm to identify subreddits discussing nonmedical opioid consumption and developed a methodology based on word embedding to find alternative colloquial and nonmedical terms referring to opioid substances, routes of administration, and drug-tampering methods. We modeled the preferences of adoption of substances and routes of administration, estimating their prevalence and temporal unfolding. Ultimately, through the evaluation of odds ratios based on co-mentions, we measured the strength of association between opioid substances, routes of administration, and drug tampering. RESULTS: We identified 32 subreddits discussing nonmedical opioid usage from 2014 to 2018 and observed the evolution of interest among over 86,000 Reddit users potentially involved in firsthand opioid usage. We learned the language model of opioid consumption and provided alternative vocabularies for opioid substances, routes of administration, and drug tampering. A data-driven taxonomy of nonmedical routes of administration was proposed. We modeled the temporal evolution of interest in opioid consumption by ranking the popularity of the adoption of opioid substances and routes of administration, observing relevant trends, such as the surge in synthetic opioids like fentanyl and an increasing interest in rectal administration. In addition, we measured the strength of association between drug tampering, routes of administration, and substance consumption, finding evidence of understudied abusive behaviors, like chewing fentanyl patches and dissolving buprenorphine sublingually. CONCLUSIONS: This work investigated some important consumption-related aspects of the opioid epidemic using Reddit data. We believe that our approach may provide a novel perspective for a more comprehensive understanding of nonmedical abuse of opioids substances and inform the prevention, treatment, and control of the public health effects.

Detecting fake news by exploring the consistency of multimodal data.

During the outbreak of the new Coronavirus (2019-nCoV) in 2020, the spread of fake news has caused serious social panic. Fake news often uses multimedia information such as text and image to mislead readers, spreading and expanding its influence. One of the most important problems in fake news detection based on multimodal data is to extract the general features as well as to fuse the intrinsic characteristics of the fake news, such as mismatch of image and text and image tampering. This paper proposes a Multimodal Consistency Neural Network (MCNN) that considers the consistency of multimodal data and captures the overall characteristics of social media information. Our method consists of five subnetworks: the text feature extraction module, the visual semantic feature extraction module, the visual tampering feature extraction module, the similarity measurement module, and the multimodal fusion module. The text feature extraction module and the visual semantic feature extraction module are responsible for extracting the semantic features of text and vision and mapping them to the same space for a common representation of cross-modal features. The visual tampering feature extraction module is responsible for extracting visual physical and tamper features. The similarity measurement module can directly measure the similarity of multimodal data for the problem of mismatching of image and text. We assess the constructed method in terms of four datasets commonly used for fake news detection. The accuracy of the detection is improved clearly compared to the best available methods.

Tampering with Nature: A Systematic Review.

Tampering with nature has been shown to be a strong, and sometimes even the strongest, predictor of the risk perception and acceptance of various technologies and behaviors, including environmental technologies, such as geoengineering. It is therefore helpful to understand what tampering with nature is as a construct, to which factors it relates, and when a technology or behavior is perceived as such. By means of a systematic review, we show that very little systematic research has been conducted on tampering with nature. Because tampering with nature has not yet been clearly defined, no systematic operationalization of tampering with nature has been used in the current literature. We show that tampering with nature is often used interchangeably with other constructs, such as naturalness. Based on the literature, we suggest that tampering with nature is related to and possibly influenced by three other constructs, which are naturalness, morality, and controllability. We discuss the influence of tampering with nature on the acceptance and risk perception of various technologies and behaviors and make suggestions for future research needs in order to better understand this construct.

Image Forgery Detection and Localization via a Reliability Fusion Map.

Moving away from hand-crafted feature extraction, the use of data-driven convolution neural network (CNN)-based algorithms facilitates the realization of end-to-end automated forgery detection in multimedia forensics. On the basis of fingerprints acquired by images from different camera models, the goal of this paper is to design an effective detector capable of completing image forgery detection and localization. Specifically, relying on the designed constant high-pass filter, we first establish a well-performing CNN architecture to adaptively and automatically extract characteristics, and design a reliability fusion map (RFM) to improve localization resolution, and tamper detection accuracy. The extensive results from our empirical experiments demonstrate the effectiveness of our proposed RFM-based detector, and its better performance than other competing approaches.

Identification of factors influencing tampering of codeine-containing medicines in England: a qualitative study.

BACKGROUND: Tampering of psychoactive medicines presents challenges to regulation and public health. However, little is currently known about what influences the decisions to treat codeine-containing medicines (CCM) with cold water extraction (CWE) from the perspective of individuals employing these techniques. The article identifies factors influencing utilisation of CWE to separate codeine from compounded analgesics, such as paracetamol and ibuprofen, found in CCM. METHODS: Purposive sampling of 27 participants residing in England who took part in a qualitative interview. Of these, 14 individuals (11 males and 3 females) reported tampering of psychoactive medicines, and the relevant transcripts were included in the analyses for the study. Participants were recruited from one addiction treatment service and from an online survey. The mean age of the participants was 31.5 years (range = 18-42 years). Qualitative data analysis followed the processes of iterative categorization (IC). The codes 'harm reduction', 'information sources' and 'changes on the drug markets' were grouped and summarised. The coding of the data was done in a Microsoft® Word document. RESULTS: Two groups of participants were identified in the data analysis: (i) individuals who used CCM (n = 5), and (ii) individuals who used CCM and heroin (n = 9). Participants in both groups used CWE due to concerns of paracetamol overdose from the use of excessive dosages of CCM. For both of them, information obtained from the internet encouraged the use of CWE. Participants using CCM described how the many steps involved in conducting CWE, including sourcing codeine boxes from pharmacies (over the counter), presented a barrier against using CWE. Participants using CCM and heroin explained how reduced availability in the local heroin supply influenced utilisation of CWE techniques to maintain their use of opioids and avoid withdrawal. Withdrawal symptoms and cravings outweighed the concerns about the quality of the extracted codeine mixtures in this participant group, especially the ability of CWE to remove paracetamol and tablet fillers. CONCLUSIONS: Utilisation of CWE of codeine was influenced by several factors including drug market supply, the availability of detailed information on the internet about CWE and restrictions on codeine sourcing in pharmacies. Risks identified with CWE include consumption of unknown doses of paracetamol if the CWE techniques are not used correctly. Attempts at extracting codeine from CCM should be considered in risk assessments of opioid medicines.

Recent advances in digital image manipulation detection techniques: A brief review.

A large number of digital photos are being generated and with the help of advanced image editing software and image altering tools, it is very easy to manipulate a digital image nowadays. These manipulated or tampered images can be used to delude the public, defame a person's personality and business as well, change political views or affect the criminal investigation. The raw image can be mutilated in parts or as a whole image so there is a need for detection of what type of image tampering is performed and then localize the tampered region. Initially, single handcrafted manipulated images were used to detect the only image tampering present in the image but in a real-world scenario, a single image can be mutilated by numerous image manipulation techniques. Nowadays, multiple tampering operations are performed on the image and post-processing is done to erase the traces left behind by the tampering operation, making it more difficult for the detector to detect the tampering. It is seen that the recent techniques that are used to detect image manipulation are based on deep learning methods. In this paper, more focus is on the study of various recent image manipulation detection techniques. We have examined various image forgeries that can be performed on the image and various image manipulation detection and localization methods.

Support for the Deployment of Climate Engineering: A Comparison of Ten Different Technologies.

Due to the renewed increase in CO2 emissions seen in recent years, the deployment of climate engineering technologies might become necessary if the global temperature increase is to be kept within 1.5 °C. If climate engineering is to be deployed, however, public support is required. The present study hence compared public support for a broad range of climate engineering technologies. Further, the factors that drive public support were investigated and compared across the technologies. In an online survey conducted in Switzerland, respondents (n = 1,575) were randomly allocated to the description of 1 of 10 climate engineering technologies, of which 7 were specific carbon dioxide removal measures and 3 were solar radiation management measures. The results show that the level of public support for afforestation was the highest. The levels of public support for the other climate engineering technologies were relatively similar, although a tendency for solar radiation management to have a lower level of support was identified. Across all the investigated climate engineering technologies, the perceived benefits were an important driver of public support. Additionally, for all the technologies but afforestation, a higher level of trust in industry/science/government increased the level of public support, whereas the factor perceived risks and tampering with nature was found to be a negative predictor of support. The present findings suggest that there are opportunities available for the deployment of several climate engineering technologies in combination with other mitigation measures. Communicating the benefits of such technologies might be an effective strategy in terms of fostering increased support.