As video analysis using deep learning models becomes more widespread, the
vulnerability of such models to adversarial attacks is becoming a pressing
concern. In particular, Universal Adversarial Perturbation (UAP) poses a
significant threat, as a single perturbation can mislead deep learning models
on entire datasets. We propose a novel video UAP using image data and image
model. This enables us to take advantage of the rich image data and image
model-based studies available for video applications. However, there is a
challenge that image models are limited in their ability to analyze the
temporal aspects of videos, which is crucial for a successful video attack. To
address this challenge, we introduce the Breaking Temporal Consistency (BTC)
method, which is the first attempt to incorporate temporal information into
video attacks using image models. We aim to generate adversarial videos that
have opposite patterns to the original. Specifically, BTC-UAP minimizes the
feature similarity between neighboring frames in videos. Our approach is simple
but effective at attacking unseen video models. Additionally, it is applicable
to videos of varying lengths and invariant to temporal shifts. Our approach
surpasses existing methods in terms of effectiveness on various datasets,
including ImageNet, UCF-101, and Kinetics-400.

使用图像数据和图像模型的 Breaking Temporal Consistency（BTC）方法，通过对视频中邻近帧的特征相似性进行最小化以生成逆向模式的对抗性视频，在各种数据集中具有优越的效果。