Fast Adversarial CNN-based Perturbation Attack on No-Reference Image-
  and Video-Quality Metrics
Ekaterina Shumitskaya Anastasia Antsiferova Dmitriy Vatolin
Modern neural-network-based no-reference image- and video-quality metrics
exhibit performance as high as full-reference metrics. These metrics are widely
used to improve visual quality in computer vision methods and compare video
processing methods. However, these metrics are not stable to traditional
adversarial attacks, which can cause incorrect results. Our goal is to
investigate the boundaries of no-reference metrics applicability, and in this
paper, we propose a fast adversarial perturbation attack on no-reference
quality metrics. The proposed attack (FACPA) can be exploited as a
preprocessing step in real-time video processing and compression algorithms.
This research can yield insights to further aid in designing of stable
neural-network-based no-reference quality metrics.