论文标题
对抗性稳健的框架采样，有界不规则
Adversarially Robust Frame Sampling with Bounded Irregularities
论文作者
论文摘要
近年来，广泛研究和部署了用于自动从视频中提取有意义信息的视频分析工具。因为他们中的大多数人都使用计算上昂贵的深神经网络，因此需要将视频帧的一部分馈入此类算法。以固定速率对框架进行采样总是有吸引力的，因为它的简单性，代表性和解释性。例如，流行的云视频API仅处理视频中每一秒的第一帧就生成了视频和拍摄标签。但是，可以通过将所选框架放置在采样位置来轻松攻击此类策略。在本文中，我们为这种抽样问题提供了一种优雅的解决方案，该解决方案证明对对抗性攻击非常强大，并引入了有限的不规则性。
In recent years, video analysis tools for automatically extracting meaningful information from videos are widely studied and deployed. Because most of them use deep neural networks which are computationally expensive, feeding only a subset of video frames into such algorithms is desired. Sampling the frames with fixed rate is always attractive for its simplicity, representativeness, and interpretability. For example, a popular cloud video API generated video and shot labels by processing only the first frame of every second in a video. However, one can easily attack such strategies by placing chosen frames at the sampled locations. In this paper, we present an elegant solution to this sampling problem that is provably robust against adversarial attacks and introduces bounded irregularities as well.
