Abstract
Recent advances in large-scale video models have significantly improved video understanding across domains such as surveillance, healthcare, and entertainment. However, these models also amplify privacy risks by encoding sensitive attributes, including facial identity, race, and gender. While image anonymization has been extensively studied, video anonymization remains relatively underexplored, even though modern video models can leverage spatiotemporal motion patterns as biometric identifiers. To address this challenge, we propose a novel attention-driven spatiotemporal video anonymization framework based on systematic disentanglement of utility and privacy features. Our key insight is that attention mechanisms in Vision Transformers (ViTs) can be explicitly structured to separate action-relevant information from privacy-sensitive content. Building on this insight, we introduce two task-specific classification tokens, an action CLS token and a privacy CLS token, that learn complementary representations within a shared Transformer backbone. We contrast their attention distributions to compute a utility-privacy score for each spatiotemporal tubelet, and keep the top-k tubelets with the highest scores. This selectively prunes tubelets dominated by privacy cues while preserving those most critical for action recognition. Extensive experiments demonstrate that our approach maintains action recognition performance comparable to models trained on raw videos, while substantially reducing privacy leakage. These results indicate that attention-driven spatiotemporal pruning offers an effective and principled solution for privacy-preserving video analytics.
Abstract (translated)
大规模视频模型的最新进展显著提升了监控、医疗和娱乐等领域的视频理解能力。然而，这些模型也通过编码面部身份、种族和性别等敏感属性而放大了隐私风险。尽管图像匿名化技术已得到广泛研究，视频匿名化仍相对缺乏探索——而现代视频模型能够利用时空运动模式作为生物特征标识符。为应对这一挑战，我们提出了一种基于效用与隐私特征系统性解耦的、新型注意力驱动时空视频匿名化框架。我们的核心见解是：视觉Transformer（ViTs）中的注意力机制可被显式结构化，以分离与动作相关的信息与隐私敏感内容。基于此见解，我们引入了两个任务特定的分类标记——动作CLS标记与隐私CLS标记，使它们能在共享Transformer主干网络中学习互补表征。我们通过对比它们的注意力分布，为每个时空小片段计算效用-隐私分数，并保留分数最高的前k个小片段。这种方法能选择性地剪除由隐私线索主导的小片段，同时保留对动作识别最关键的部分。大量实验表明，我们的方法在保持与原始视频训练模型相当的动作识别性能的同时，显著降低了隐私泄露风险。这些结果表明，注意力驱动的时空剪枝为隐私保护视频分析提供了有效且 principled 的解决方案。
URL
https://arxiv.org/abs/2603.26336
