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Neural networks are now actively being used for computer vision tasks insecurity critical areas such as robotics, face recognition, autonomous vehiclesyet their safety is under question after the discovery of adversarial attacks.In this paper we develop simplified adversarial attack algorithms based on ascoping idea, which enables execution of fast adversarial attacks that minimizestructural image quality (SSIM) loss, allows performing efficient transferattacks with low target inference network call count and opens a possibility ofan attack using pen-only drawings on a paper for the MNIST handwritten digitdataset. The presented adversarial attack analysis and the idea of attackscoping can be easily expanded to different datasets, thus making the paper'sresults applicable to a wide range of practical tasks.

Minimizing Perceived Image Quality Loss Through Adversarial Attack Scoping