arXiv:2508.14581v1 Announce Type: new
Abstract: FakeHunter is a multimodal deepfake detection framework that combines memory-guided retrieval, chain-of-thought (Observation-Thought-Action) reasoning, and tool-augmented verification to provide accurate and interpretable video forensics. FakeHunter encodes visual content using CLIP and audio using CLAP, generating joint audio-visual embeddings that retrieve semantically similar real exemplars from a FAISS-indexed memory bank for contextual grounding. Guided by the retrieved context, the system iteratively reasons over evidence to localize manipulations and explain them. When confidence is low, it automatically invokes specialized tools-such as zoom-in image forensics or mel-spectrogram inspection-for fine-grained verification. Built on Qwen2.5-Omni-7B, FakeHunter produces structured JSON verdicts that specify what was modified, where it occurs, and why it is judged fake. We also introduce X-AVFake, a benchmark comprising 5.7k+ manipulated and real videos (950+ min) annotated with manipulation type, region/entity, violated reasoning category, and free-form justification. On X-AVFake, FakeHunter achieves an accuracy of 34.75%, outperforming the vanilla Qwen2.5-Omni-7B by 16.87 percentage points and MiniCPM-2.6 by 25.56 percentage points. Ablation studies reveal that memory retrieval contributes a 7.75 percentage point gain, and tool-based inspection improves low-confidence cases to 46.50%. Despite its multi-stage design, the pipeline processes a 10-minute clip in 8 minutes on a single NVIDIA A800 (0.8x real-time) or 2 minutes on four GPUs (0.2x), demonstrating practical deployability.
FakeHunter: A Multimodal Deepfake Detection Framework
The article discusses the development of FakeHunter, a multimodal deepfake detection framework that utilizes a combination of memory-guided retrieval, chain-of-thought reasoning, and tool-augmented verification to enhance video forensics. This framework represents an innovative approach to combating the proliferation of deepfake content, which has increasingly become a significant concern in the era of digital manipulation.
One of the key aspects of FakeHunter is its integration of CLIP for visual content encoding and CLAP for audio encoding, enabling the generation of joint audio-visual embeddings that facilitate the retrieval of semantically similar real exemplars for contextual grounding. This multi-disciplinary approach draws on techniques from computer vision, natural language processing, and machine learning to improve the accuracy and interpretability of deepfake detection.
Application in Multimedia Information Systems
FakeHunter’s utilization of memory-guided retrieval and chain-of-thought reasoning aligns with concepts commonly found in multimedia information systems, where the integration of multiple modalities such as text, images, and video is essential for effective data analysis and retrieval. By incorporating these principles, FakeHunter demonstrates the potential for advancing multimedia forensics and data verification techniques in a rapidly evolving digital landscape.
Connection to Artificial Reality, Augmented Reality, and Virtual Realities
The development of deepfake detection technologies like FakeHunter has significant implications for the fields of Artificial Reality, Augmented Reality, and Virtual Realities. As the boundaries between reality and digital fabrication continue to blur, the ability to accurately distinguish between authentic and manipulated content is crucial for preserving trust and credibility in virtual environments. FakeHunter’s use of specialized tools and iterative reasoning processes reflects a growing trend towards enhancing the authenticity and integrity of digital experiences.
In conclusion, FakeHunter represents a significant advancement in the field of deepfake detection, showcasing the potential of multi-disciplinary approaches to address complex challenges in digital media manipulation. By incorporating diverse techniques and methodologies, FakeHunter paves the way for future innovations in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.