Adversarial Attacks on Video Object Segmentation with Hard Region
  Discovery
Ping Li Yu Zhang Li Yuan Jian Zhao Xianghua Xu Xiaoqin Zhang
Video object segmentation has been applied to various computer vision tasks,
such as video editing, autonomous driving, and human-robot interaction.
However, the methods based on deep neural networks are vulnerable to
adversarial examples, which are the inputs attacked by almost
human-imperceptible perturbations, and the adversary (i.e., attacker) will fool
the segmentation model to make incorrect pixel-level predictions. This will
rise the security issues in highly-demanding tasks because small perturbations
to the input video will result in potential attack risks. Though adversarial
examples have been extensively used for classification, it is rarely studied in
video object segmentation. Existing related methods in computer vision either
require prior knowledge of categories or cannot be directly applied due to the
special design for certain tasks, failing to consider the pixel-wise region
attack. Hence, this work develops an object-agnostic adversary that has
adversarial impacts on VOS by first-frame attacking via hard region discovery.
Particularly, the gradients from the segmentation model are exploited to
discover the easily confused region, in which it is difficult to identify the
pixel-wise objects from the background in a frame. This provides a hardness map
that helps to generate perturbations with a stronger adversarial power for
attacking the first frame. Empirical studies on three benchmarks indicate that
our attacker significantly degrades the performance of several state-of-the-art
video object segmentation models.