AVFakeBench: A Comprehensive Audio-Video Forgery Detection Benchmark for AV-LMMs
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910051264888832 |
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| author | Xia, Shuhan Li, Peipei Liu, Xuannan Zhang, Dongsen Guo, Xinyu Li, Zekun |
| author_facet | Xia, Shuhan Li, Peipei Liu, Xuannan Zhang, Dongsen Guo, Xinyu Li, Zekun |
| contents | The threat of Audio-Video (AV) forgery is rapidly evolving beyond human-centric deepfakes to include more diverse manipulations across complex natural scenes. However, existing benchmarks are still confined to DeepFake-based forgeries and single-granularity annotations, thus failing to capture the diversity and complexity of real-world forgery scenarios. To address this, we introduce AVFakeBench, the first comprehensive audio-video forgery detection benchmark that spans rich forgery semantics across both human subject and general subject. AVFakeBench comprises 12K carefully curated audio-video questions, covering seven forgery types and four levels of annotations. To ensure high-quality and diverse forgeries, we propose a multi-stage hybrid forgery framework that integrates proprietary models for task planning with expert generative models for precise manipulation. The benchmark establishes a multi-task evaluation framework covering binary judgment, forgery types classification, forgery detail selection, and explanatory reasoning. We evaluate 11 Audio-Video Large Language Models (AV-LMMs) and 2 prevalent detection methods on AVFakeBench, demonstrating the potential of AV-LMMs as emerging forgery detectors while revealing their notable weaknesses in fine-grained perception and reasoning. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_21251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AVFakeBench: A Comprehensive Audio-Video Forgery Detection Benchmark for AV-LMMs Xia, Shuhan Li, Peipei Liu, Xuannan Zhang, Dongsen Guo, Xinyu Li, Zekun Computer Vision and Pattern Recognition The threat of Audio-Video (AV) forgery is rapidly evolving beyond human-centric deepfakes to include more diverse manipulations across complex natural scenes. However, existing benchmarks are still confined to DeepFake-based forgeries and single-granularity annotations, thus failing to capture the diversity and complexity of real-world forgery scenarios. To address this, we introduce AVFakeBench, the first comprehensive audio-video forgery detection benchmark that spans rich forgery semantics across both human subject and general subject. AVFakeBench comprises 12K carefully curated audio-video questions, covering seven forgery types and four levels of annotations. To ensure high-quality and diverse forgeries, we propose a multi-stage hybrid forgery framework that integrates proprietary models for task planning with expert generative models for precise manipulation. The benchmark establishes a multi-task evaluation framework covering binary judgment, forgery types classification, forgery detail selection, and explanatory reasoning. We evaluate 11 Audio-Video Large Language Models (AV-LMMs) and 2 prevalent detection methods on AVFakeBench, demonstrating the potential of AV-LMMs as emerging forgery detectors while revealing their notable weaknesses in fine-grained perception and reasoning. |
| title | AVFakeBench: A Comprehensive Audio-Video Forgery Detection Benchmark for AV-LMMs |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.21251 |