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In audio copy-move forgery forensics, existing traditional methods typically first segment audio into voiced and silent segments, then compute the similarity between voiced segments to detect and locate forged segments. However, audio collected in noisy environments is difficult to segment and manually set, and heuristic similarity thresholds lack robustness. Existing deep learning methods extract features from audio and then use neural networks for binary classification, lacking the ability to locate forged segments. Therefore, for locating audio copy-move forgery segments, we have improved deep learning methods and proposed a robust localization model by CNN-based spectral analysis. In the localization model, the Feature Extraction Module extracts deep features from Mel-spectrograms, while the Correlation Detection Module automatically decides on the correlation between these deep features. Finally, the Mask Decoding Module visually locates the forged segments. Experimental results show that compared to existing methods, the localization model improves the detection accuracy of audio copy-move forgery by 3.0–6.8%and improves the average detection accuracy of forged audio with post-processing attacks such as noise, filtering, resampling, and MP3 compression by over 7.0%. [ABSTRACT FROM AUTHOR]

With the advancement of generative models, face forgeries are becoming increasingly realistic, making face forgery detection a hot topic in research. The primary challenge in face forgery detection is the inadequate generalization performance. Numerous studies have proposed solutions to this issue; however, some methods heavily rely on the overall feature space of training samples, interfering with the extraction of key features for detection. Additionally, some studies design disentangled frameworks that overlook data diversity, limiting their effectiveness in complex real-world scenarios. This paper presents a model framework based on adversarial training and disentanglement strategy. Adversarial training is employed to generate forged samples that imitate the face forgery process, specifically targeting certain facial areas to simulate face forgery effects, which enriches data diversity. Simultaneously, the feature disentanglement strategies are employed to focus the model on forgery features, with a mutual information loss function designed to obtain the disentanglement effect. Additionally, an adversarial loss based on mutual information is designed to further enhance the disentanglement effect. On the FaceForensics++ dataset, our method achieves an AUC of 96.75%. Simultaneously, it demonstrates outstanding performance in cross-method evaluations with an accuracy of 80.32%. In cross-dataset experiments, our method also exhibits excellent performance. [ABSTRACT FROM AUTHOR]

Due to the openness of the vehicle-to-grid (V2G) network, the upload of charging and discharging data faces severe security challenges such as eavesdropping, tampering, and forgery. These challenges can lead to privacy breaches, transmission delays, and service quality degradation. To address these issues, a V2G network architecture based on cloud–fog-end is designed, and a charging and discharging data privacy protection scheme is proposed. We employ a pseudonym mechanism to achieve the conditional privacy protection of electric vehicle (EV) users. We design a certificateless aggregate signcryption (CLASC) algorithm to guarantee the security of uploading the charging and discharging privacy data. The algorithm solves certificate management and key escrow issues, utilizes aggregate signature operations to save network bandwidth, and avoids complex computations like bilinear pairings and exponents. Additionally, the scheme delegates the aggregate verification process to the fog layer, thereby alleviating the computational burden on the cloud layer, decreasing transmission delays, and enhancing the efficiency and reliability of the V2G network. The analysis results indicate that the scheme not only meets the required security objectives, but also has lower computational and communication overheads, making it suitable for scenarios involving the charging and discharging of large-scale EVs in V2G networks. [ABSTRACT FROM AUTHOR]

In recent years, significant advancements in the field of machine learning have influenced the domain of image restoration. While these technological advancements present prospects for improving the quality of images, they also present difficulties, particularly the proliferation of manipulated or counterfeit multimedia information on the internet. The objective of this paper is to provide a comprehensive review of existing inpainting algorithms and forgery detections, with a specific emphasis on techniques that are designed for the purpose of removing objects from digital images. In this study, we will examine various techniques encompassing conventional texture synthesis methods as well as those based on neural networks. Furthermore, we will present the artifacts frequently introduced by the inpainting procedure and assess the state-of-the-art technology for detecting such modifications. Lastly, we shall look at the available datasets and how the methods compare with each other. Having covered all the above, the outcome of this study is to provide a comprehensive perspective on the abilities and constraints of detecting object removal via the inpainting procedure in images. [ABSTRACT FROM AUTHOR]

As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender's identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender's identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value). [ABSTRACT FROM AUTHOR]

Image manipulation is easier than ever, often facilitated using accessible AI-based tools. This poses significant risks when used to disseminate disinformation, false evidence, or fraud, which highlights the need for image forgery detection and localization methods to combat this issue. While some recent detection methods demonstrate good performance, there is still a significant gap to be closed to consistently and accurately detect image manipulations in the wild. This paper aims to enhance forgery detection and localization by combining existing detection methods that complement each other. First, we analyze these methods' complementarity, with an objective measurement of complementariness, and calculation of a target performance value using a theoretical oracle fusion. Then, we propose a novel fusion method that combines the existing methods' outputs. The proposed fusion method is trained using a Generative Adversarial Network architecture. Our experiments demonstrate improved detection and localization performance on a variety of datasets. Although our fusion method is hindered by a lack of generalization, this is a common problem in supervised learning, and hence a motivation for future work. In conclusion, this work deepens our understanding of forgery detection methods' complementariness and how to harmonize them. As such, we contribute to better protection against image manipulations and the battle against disinformation. [ABSTRACT FROM AUTHOR]