The field of digital media forensics is rapidly evolving, with a growing focus on detecting and interpreting visual forgeries. Recent research has emphasized the importance of developing robust frameworks for image and video forgery detection, as well as improving the generalization capabilities of detectors across different domains and scenarios. Notable advancements include the use of semantic discrepancy-aware detectors, vision-language models, and multimodal step-by-step reasoning for explainable video forensics. These innovative approaches have shown promising results in identifying and localizing manipulations in digital media. Noteworthy papers include: The paper proposing the Semantic Discrepancy-aware Detector achieves superior results compared to existing methods. The REVEAL framework incorporates generalized guidelines and provides reasoning as well as localization for image forgery detection. The FakeHunter framework combines memory-guided retrieval, chain-of-thought reasoning, and tool-augmented verification for accurate and interpretable video forensics. The Paired-Sampling Contrastive Framework achieves an average classification error rate of 2.10 percent on the 6th Face Anti-Spoofing Challenge Unified Physical-Digital Attack Detection benchmark.