Ti-Patch: Tiled Physical Adversarial Patch for no-reference video
  quality metrics
Victoria Leonenkova Ekaterina Shumitskaya Anastasia Antsiferova Dmitriy Vatolin
Objective no-reference image- and video-quality metrics are crucial in many
computer vision tasks. However, state-of-the-art no-reference metrics have
become learning-based and are vulnerable to adversarial attacks. The
vulnerability of quality metrics imposes restrictions on using such metrics in
quality control systems and comparing objective algorithms. Also, using
vulnerable metrics as a loss for deep learning model training can mislead
training to worsen visual quality. Because of that, quality metrics testing for
vulnerability is a task of current interest. This paper proposes a new method
for testing quality metrics vulnerability in the physical space. To our
knowledge, quality metrics were not previously tested for vulnerability to this
attack; they were only tested in the pixel space. We applied a physical
adversarial Ti-Patch (Tiled Patch) attack to quality metrics and did
experiments both in pixel and physical space. We also performed experiments on
the implementation of physical adversarial wallpaper. The proposed method can
be used as additional quality metrics in vulnerability evaluation,
complementing traditional subjective comparison and vulnerability tests in the
pixel space. We made our code and adversarial videos available on GitHub:
https://github.com/leonenkova/Ti-Patch.