SWIFT: Sliding Window Reconstruction for Few-Shot Training-Free Generated Video Attribution

TL;DR

SWIFT is a novel, training-free method for attributing generated videos to their source using a sliding window approach that leverages temporal features, achieving high accuracy with minimal samples.

Contribution

This paper introduces SWIFT, the first approach for few-shot, training-free video attribution that exploits temporal mappings within videos for effective source identification.

Findings

Achieves over 90% attribution accuracy with only 20 samples

Enables zero-shot attribution for multiple video generation models

Does not degrade video quality or require extensive training

Abstract

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.