BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation
This addresses misinformation risks from realistic AI-generated videos for social media users and content verifiers, though it is incremental as it builds on existing detection methods by adding explainability.
The authors tackled the problem of detecting AI-generated video forgeries by creating GenBuster-200K, a large-scale dataset of 200K high-resolution video clips, and proposing BusterX, a framework that uses multimodal large language models and reinforcement learning to provide detection with explanations, achieving effectiveness and generalizability in experiments.
Advances in AI generative models facilitate super-realistic video synthesis, amplifying misinformation risks via social media and eroding trust in digital content. Several research works have explored new deepfake detection methods on AI-generated images to alleviate these risks. However, with the fast development of video generation models, such as Sora and WanX, there is currently a lack of large-scale, high-quality AI-generated video datasets for forgery detection. In addition, existing detection approaches predominantly treat the task as binary classification, lacking explainability in model decision-making and failing to provide actionable insights or guidance for the public. To address these challenges, we propose \textbf{GenBuster-200K}, a large-scale AI-generated video dataset featuring 200K high-resolution video clips, diverse latest generative techniques, emphasis on fairness, and focus on real-world scenes. We further introduce \textbf{BusterX}, a novel AI-generated video detection and explanation framework leveraging multimodal large language model (MLLM) and reinforcement learning (RL) to provide authenticity determination and explainable rationales. To our knowledge, BusterX is the first framework to integrate MLLM with RL for explainable AI-generated video detection. Extensive experiments with state-of-the-art methods and ablation studies demonstrate the effectiveness and generalizability of BusterX.