VideoGuard：保护视频内容免受未经授权的编辑
VideoGuard: Protecting Video Content from Unauthorized Editing
AI总结：
针对视频内容易被生成模型恶意编辑的问题，提出VideoGuard方法，通过联合帧优化和运动信息融合生成微扰，有效保护视频，性能优于基线。
摘要：
随着生成技术的快速发展，当前的生成模型能够生成高保真度的数字内容，并以受控方式进行编辑。然而，存在恶意个人滥用这些能力进行误导性活动的风险。尽管现有研究已尝试保护摄影图像免受生成模型的操纵，但在视频内容编辑方面所提供的保护仍存在显著差距。为弥合这一差距，我们提出了一种名为VideoGuard的保护方法，该方法能有效保护视频免受未经授权的恶意编辑。这种保护是通过微妙地引入几乎不可察觉的扰动来实现的，这些扰动会干扰预期生成扩散模型的功能。由于视频帧之间的冗余性以及视频扩散模型中的帧间注意力机制，简单地将基于图像的保护方法分别应用于每一视频帧无法保护视频免受未经授权的编辑。为应对上述挑战，我们采用联合帧优化，将所有视频帧视为一个优化实体。此外，我们提取视频运动信息并将其融合到优化目标中。因此，这些改动能有效迫使模型产生不合理且不一致的输出。我们提供了一个优化此扰动的流程。最后，我们使用客观指标和主观指标来证明我们方法的有效性，结果表明VideoGuard的保护性能优于所有基线方法。
英文摘要：
With the rapid development of generative technology, current generative models can generate high-fidelity digital content and edit it in a controlled manner. However, there is a risk that malicious individuals might misuse these capabilities for misleading activities. Although existing research has attempted to shield photographic images from being manipulated by generative models, there remains a significant disparity in the protection offered to video content editing. To bridge the gap, we propose a protection method named VideoGuard, which can effectively protect videos from unauthorized malicious editing. This protection is achieved through the subtle introduction of nearly unnoticeable perturbations that interfere with the functioning of the intended generative diffusion models. Due to the redundancy between video frames, and inter-frame attention mechanism in video diffusion models, simply applying image-based protection methods separately to every video frame can not shield video from unauthorized editing. To tackle the above challenge, we adopt joint frame optimization, treating all video frames as an optimization entity. Furthermore, we extract video motion information and fuse it into optimization objectives. Thus, these alterations can effectively force the models to produce outputs that are implausible and inconsistent. We provide a pipeline to optimize this perturbation. Finally, we use both objective metrics and subjective metrics to demonstrate the efficacy of our method, and the results show that the protection performance of VideoGuard is superior to all the baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
