AI视频检测的盲区：能判真假，却说不出哪里假
现在的AI视频检测器有个尴尬：它能告诉你视频是假的，但你要是问它“哪里假”，它常常答不上来，或者指错地方。这篇研究把“找破绽”拆成三步——先决定该查哪儿、再验证那儿是不是真有生成痕迹、最后把证据汇总成结论，并用人类标注的细节来训练模型。结果它不仅能判真假，还能指出具体是哪个像素区域露了馅，比现有方法更准。它不是你明天能用上的工具，但这是AI视频检测从“黑盒打分”走向“可解释指证”的一步。
📄 原文摘要(英文)
Recent advances in video generation have made synthetic videos increasingly realistic, placing greater demands on reliable AI-generated video detection. Existing artifact-based detectors typically identify generation artifacts as evidence for source prediction, yet they may either fail to recognize subtle artifacts or reach the correct source decision without faithfully identifying the evidence underlying it. In this work, we systematically investigate how to design and train effective artifact detectors and formulate artifact detection as a structured process comprising three complementary capabilities: Inspection Planning, which determines what should be examined; Evidence Verification, which verifies whether the inspected content exhibits genuine generation artifacts; and Evidence Aggregation, which integrates the verified evidence into the final source decision. Building on this formulation, we introduce Verus-Insight, trained with human-anchored full-process supervision and progressive capability training to strengthen both fine-grained artifact detection and video-level detection. We further introduce Verus-Bench, a benchmark with fine-grained human annotations of perceptible artifacts for evaluating artifact detection. Extensive experiments across diverse benchmarks demonstrate that Verus-Insight achieves strong performance in both fine-grained artifact analysis and AI-generated video detection, with substantial gains in recovering human-perceived artifacts. Further analyses reveal that the three capabilities are complementary yet distinct, and that Verus-Insight more reliably converts relevant inspection targets into supported artifact evidence without increasing spurious artifact claims.