  Universal adversarial perturbation attacks are widely used to analyze image
classifiers that employ convolutional neural networks. Nowadays, some attacks
can deceive image- and video-quality metrics. So sustainability analysis of
these metrics is important. Indeed, if an attack can confuse the metric, an
attacker can easily increase quality scores. When developers of image- and
video-algorithms can boost their scores through detached processing, algorithm
comparisons are no longer fair. Inspired by the idea of universal adversarial
perturbation for classifiers, we suggest a new method to attack differentiable
no-reference quality metrics through universal perturbation. We applied this
method to seven no-reference image- and video-quality metrics (PaQ-2-PiQ,
Linearity, VSFA, MDTVSFA, KonCept512, Nima and SPAQ). For each one, we trained
a universal perturbation that increases the respective scores. We also propose
a method for assessing metric stability and identify the metrics that are the
most vulnerable and the most resistant to our attack. The existence of
successful universal perturbations appears to diminish the metric's ability to
provide reliable scores. We therefore recommend our proposed method as an
additional verification of metric reliability to complement traditional
subjective tests and benchmarks.
