Learning Human-Perceived Fakeness in AI-Generated Videos via Multimodal
  LLMs
Authors: name: Xingyu Fu; name: Siyi Liu; name: Yinuo Xu; name: Pan Lu; name: Guangqiuse Hu; name: Tianbo Yang; name: Taran Anantasagar; name: Christopher Shen; name: Yikai Mao; name: Yuanzhe Liu; name: Keyush Shah; name: Chung Un Lee; name: Yejin Choi; name: James Zou; name: Dan Roth; name: Chris Callison-Burch
Published: 2025-09-26 17:59:54

Abstract: Can humans identify AI-generated (fake) videos and provide grounded reasons?
While video generation models have advanced rapidly, a critical dimension --
whether humans can detect deepfake traces within a generated video, i.e.,
spatiotemporal grounded visual artifacts that reveal a video as machine
generated -- has been largely overlooked. We introduce DeeptraceReward, the
first fine-grained, spatially- and temporally- aware benchmark that annotates
human-perceived fake traces for video generation reward. The dataset comprises
4.3K detailed annotations across 3.3K high-quality generated videos. Each
annotation provides a natural-language explanation, pinpoints a bounding-box
region containing the perceived trace, and marks precise onset and offset
timestamps. We consolidate these annotations into 9 major categories of
deepfake traces that lead humans to identify a video as AI-generated, and train
multimodal language models (LMs) as reward models to mimic human judgments and
localizations. On DeeptraceReward, our 7B reward model outperforms GPT-5 by
34.7% on average across fake clue identification, grounding, and explanation.
Interestingly, we observe a consistent difficulty gradient: binary fake v.s.
real classification is substantially easier than fine-grained deepfake trace
detection; within the latter, performance degrades from natural language
explanations (easiest), to spatial grounding, to temporal labeling (hardest).
By foregrounding human-perceived deepfake traces, DeeptraceReward provides a
rigorous testbed and training signal for socially aware and trustworthy video
generation.