VideoMarkBench: Benchmarking Robustness of Video Watermarking
Zhengyuan Jiang, Moyang Guo, Kecen Li, Yuepeng Hu, Yupu Wang, Zhicong Huang, Cheng Hong, Neil Zhenqiang Gong

TL;DR
VideoMarkBench provides a comprehensive benchmark for assessing the robustness of video watermarking methods against various attacks, revealing significant vulnerabilities and emphasizing the need for more resilient solutions in the context of synthetic videos.
Contribution
This work introduces the first systematic benchmark for evaluating video watermarking robustness across multiple models, styles, and attack types, filling a critical gap in the field.
Findings
Current watermarking methods are vulnerable to various perturbations.
Robustness varies significantly across different watermarking techniques.
The benchmark highlights the urgent need for developing more resilient watermarking solutions.
Abstract
The rapid development of video generative models has led to a surge in highly realistic synthetic videos, raising ethical concerns related to disinformation and copyright infringement. Recently, video watermarking has been proposed as a mitigation strategy by embedding invisible marks into AI-generated videos to enable subsequent detection. However, the robustness of existing video watermarking methods against both common and adversarial perturbations remains underexplored. In this work, we introduce VideoMarkBench, the first systematic benchmark designed to evaluate the robustness of video watermarks under watermark removal and watermark forgery attacks. Our study encompasses a unified dataset generated by three state-of-the-art video generative models, across three video styles, incorporating four watermarking methods and seven aggregation strategies used during detection. We…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Generative Adversarial Networks and Image Synthesis · Advanced Malware Detection Techniques
