ChronoLock: 保护视频免受未经授权的文本到视频个性化
ChronoLock: Protecting Videos from Unauthorized Text-to-Video Personalization
AI总结：
针对文本到视频个性化中的未经授权微调风险，提出ChronoLock框架，通过扰动时间去噪轨迹破坏运动学习过程，有效降低运动模仿质量。
摘要：
文本到视频（T2V）扩散模型使得合成逼真且时间一致的视频越来越容易，而最近的个性化技术允许此类模型仅从少量参考片段中模仿特定主体、风格或运动模式。这种能力创造了一种新的数据滥用风险：在线共享的视频可以被收集并用于未经授权的T2V微调。现有的保护性扰动主要针对图像识别或文本到图像个性化设计，因此侧重于破坏静态外观线索，而非使视频个性化成为可能的时间去噪动态。为了解决这一空白，我们引入了ChronoLock，这是第一个主动保护框架，使得发布的视频难以被利用进行未经授权的T2V个性化。ChronoLock直接针对运动学习过程，通过在时间去噪轨迹上优化有界扰动。它首先通过结合拟合误差、帧间去噪关系和相邻帧变化的扩散目标来破坏块内时间适应，然后扩大块间边界不匹配以削弱长程运动连续性。变换采样更新进一步提高了对常见预处理的鲁棒性。在UCF Sports和HMDB51上使用流行的T2V骨干网络和个性化方案的实验表明，ChronoLock在自动指标和人工评估下有效降低了运动模仿。
英文摘要：
Text-to-video (T2V) diffusion models have made it increasingly easy to synthesize realistic and temporally coherent videos, while recent personalization techniques allow such models to imitate a specific subject, style, or motion pattern from only a few reference clips. This capability creates a new data-misuse risk: videos shared online can be collected and used for unauthorized T2V fine-tuning. Existing protective perturbations are mainly designed for image recognition or text-to-image personalization, and therefore focus on corrupting static appearance cues rather than the temporal denoising dynamics that make video personalization possible. To address this gap, we introduce ChronoLock, the first proactive protection framework that makes released videos difficult to exploit for unauthorized T2V personalization. ChronoLock targets the motion-learning process directly by optimizing bounded perturbations over temporal denoising trajectories. It first disrupts intra-chunk temporal adaptation with a diffusion objective that combines fitting error, frame-relative denoising relations, and adjacent-frame variation, and then enlarges inter-chunk boundary mismatch to weaken long-range motion continuity. Transformation-sampled updates further improve robustness to common preprocessing operations.Experiments on UCF Sports and HMDB51 with popular T2V backbones and personalization scheme show that ChronoLock effectively reduces motion imitation under automatic metrics and human evaluation.
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。
