不训练模型，仅用20个样本就能高精度追溯生成视频来源。
SWIFT: Sliding Window Reconstruction for Few-Shot Training-Free Generated Video Attribution
- 用滑动窗口分析视频片段的像素到潜在特征映射变化。
- 在五种主流模型上平均准确率超90%，零样本可追踪三款模型。
- 适合需要快速溯源且无训练数据的视频安全场景。
视频生成技术快速发展，但其滥用风险日益突出，溯源生成视频来源成为关键挑战。现有方法需额外操作或训练模型，可能影响画质或依赖大量样本。本文首次定义“少样本、免训练生成视频溯源”任务，提出SWIFT方法，紧密结合视频时间特性。通过“像素帧（多）→ 潜在帧（一）”的时间映射机制，在每个视频块中使用固定长度滑动窗口执行正常与受损重建，利用两重建损失差异作为溯源信号。在五种SOTA视频生成模型上评估，仅需20个样本即实现超过90%的平均溯源准确率，并成功实现对HunyuanVideo、EasyAnimate和Wan2.2的零样本溯源。代码已开源。
原文摘要 · Abstract (English)
Recent advancements in video generation technologies have been significant, resulting in their widespread application across multiple domains. However, concerns have been mounting over the potential misuse of generated content. Tracing the origin of generated videos has become crucial to mitigate potential misuse and identify responsible parties. Existing video attribution methods require additional operations or the training of source attribution models, which may degrade video quality or necessitate large amounts of training samples. To address these challenges, we define for the first time the "few-shot training-free generated video attribution" task and propose SWIFT, which is tightly integrated with the temporal characteristics of the video. By leveraging the "Pixel Frames(many) to Latent Frame(one)" temporal mapping within each video chunk, SWIFT applies a fixed-length sliding window to perform two distinct reconstructions: normal and corrupted. The variation in the losses between two reconstructions is then used as an attribution signal. We conducted an extensive evaluation of five state-of-the-art (SOTA) video generation models. Experimental results show that SWIFT achieves over 90% average attribution accuracy with merely 20 video samples across all models and even enables zero-shot attribution for HunyuanVideo, EasyAnimate, and Wan2.2. Our source code is available at https://github.com/wangchao0708/SWIFT.
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