发表机构
School of Cyber Science and Engineering, Southeast University; Purple Mountain Laboratories; School of Computer Science and Engineering, Southeast University（东南大学网络空间科学与工程学院; 紫金山实验室; 东南大学计算机科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有视觉溯源方法泛化性不足的问题，本文提出检索驱动的无训练AI生成视频溯源范式，基于生成指纹的流程在GenVidBench上实现优于SOTA的检测与溯源性能。
AI 中文摘要
AI生成视频的逼真度不断提升，难以与真实视频区分，这为恶意滥用提供了便利，对网络安全和社会治理构成日益严重的威胁。因此，将AI生成视频溯源至其特定生成源，对于取证调查和法律监管至关重要。然而，现有的视觉溯源方法大多针对图像，且尤其依赖图像生成模型，因此缺乏对大规模AI生成视频数据的泛化能力。为解决这些局限，本文提出一种无训练的AI生成视频溯源范式。具体而言，我们将AI生成视频溯源建模为实例检索任务，并设计了一种基于生成指纹的流程。该流程包含自适应正交色彩变换、多尺度量化残差生成以及时序-语义聚合，逐步捕获并整合生成模型在视频帧中引入的伪影。在GenVidBench基准上开展的大量实验表明，我们的方法在AI生成视频检测和溯源任务中均表现出优异性能，以20.5%的Rank-1准确率和16.6%的平均精度均值，优于现有的最优方法。代码位于此https URL。
英文摘要
AI-generated videos are becoming increasingly realistic and difficult to distinguish from authentic ones, which facilitates malicious misuse and poses growing threats to cybersecurity and social governance. Attributing AI-generated videos to their specific generative sources is therefore of critical importance for forensic investigation and legal regulation. However, most existing visual attribution methods focus on images and particularly rely on the image generation model, thereby lacking the ability to generalize to large-scale AI-generated video data. To address these limitations, we introduce an training-free AI-generated video attribution paradigm. Specifically, we formulates AI-generated video attribution as an instance retrieval task, and design a generative fingerprint-based pipeline. This pipeline consists of an adapted orthogonal color transformation, multi-scale quantized residual generation, and temporal-semantic aggregation, progressively capturing and integrating artifacts introduced by generative models across video frames. Extensive experiments on the GenVidBench benchmark demonstrate that our method achieves strong performance in both AI-generated video detection and attribution, outperforming existing state-of-the-art methods with a Rank-1 accuracy of 20.5% and a mean Average Precision of 16.6%. The code is at https://github.com/renxi-seu/Video_Attribution.
