构建视频伪造检测新基准，让模型学会识破动态造假痕迹。
Beyond Static Artifacts: A Forensic Benchmark for Video Deepfake Reasoning in Vision Language Models
- 将视频伪造分析转为多选题，分三层次评估模型能力。
- 在跨数据集测试中，微调后模型准确率提升显著。
- 适合研究视频真伪判断、多模态推理的学者使用。
当前视觉语言模型在识别静态伪造痕迹方面表现良好，但忽视了视频伪造中的时间不一致问题。为此，我们提出面向伪造推理的问答基准FAQ，将时序伪造分析转化为多选任务。FAQ采用三级层次体系：（1）面部感知，检验静态视觉异常识别能力；（2）时序伪造定位，要求模型跨帧定位动态伪造痕迹；（3）伪造推理，要求模型综合证据做出真伪判断。我们在FAQ上评估多种VLM，并构建指令微调数据集FAQ-IT。实验表明，经FAQ-IT微调的模型在同域与跨数据集检测任务中均达先进水平。消融实验证实，FAQ设计是推动模型时序推理能力的核心因素。
原文摘要 · Abstract (English)
Current Vision-Language Models (VLMs) for deepfake detection excel at identifying spatial artifacts but overlook a critical dimension: temporal inconsistencies in video forgeries. Adapting VLMs to reason about these dynamic cues remains a distinct challenge. To bridge this gap, we propose Forensic Answer-Questioning (FAQ), a large-scale benchmark that formulates temporal deepfake analysis as a multiple-choice task. FAQ introduces a three-level hierarchy to progressively evaluate and equip VLMs with forensic capabilities: (1) Facial Perception, testing the ability to identify static visual artifacts; (2) Temporal Deepfake Grounding, requiring the localization of dynamic forgery artifacts across frames; and (3) Forensic Reasoning, challenging models to synthesize evidence for final authenticity verdicts. We evaluate a range of VLMs on FAQ and generate a corresponding instruction-tuning set, FAQ-IT. Extensive experiments show that models fine-tuned on FAQ-IT achieve advanced performance on both in-domain and cross-dataset detection benchmarks. Ablation studies further validate the impact of our key design choices, confirming that FAQ is the driving force behind the temporal reasoning capabilities of these VLMs.
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