The rapid advancement of AI-driven multimedia manipulation has created an urgent need for more sophisticated digital forensics solutions. Current detection methods, while effective against specific tampering types, suffer from limited generalizability across diverse manipulation techniques. To address this challenge, researchers have developed Double Compression Detection (DCD) as a universal approach through compression-domain analysis. This review presents the comprehensive analysis of DCD techniques, systematically evaluating cutting-edge techniques for audio, image, and video content forensics. The pros and cons of existing DCD schemes are summarized for the first time from the perspective of generalization and effectiveness in this review. The emerging trends and fundamental limitations of existing researches are critically examined to guide future research directions in DCD.

Deep learning models are currently under significant threat from adversarial attacks, while adversarial detection represents an effective means of countering such assaults. However, existing adversarial detection techniques are deficient in localizing video adversarial frames, leading to poor performance on sparse video adversarial attacks. This paper presents an approach for detecting adversarial perturbations in videos based on fusion features derived from the video compression and RGB domain. Our research begins by examining how the introduction of extensive non-natural noise during video adversarial attacks severely disrupts the spatial structure of individual frames and the motion information between frames. This disruption culminates in unnatural variations in the Coding Tree Units (CTU) partitioning during the HEVC video encoding process. Then meticulously mapping the positions and partitioning information of coding units (CU), predictive units (PU), and transformation units (TU) onto specific values and sizes, constituting the video’s Compression Domain Units (CDU) features. Finally, a dual-path network utilizing both the video’s CDU features and the decoded frames RGB features is employed for detecting video adversarial samples. Extensive experiments are conducted to verify the performance. The results show that the proposed scheme outperforms or rivals the state-of-the-art methods in video adversarial detection.