Componentwise Adversarial Attacks

Lucas Beerens, Desmond J. Higham

Abstract: We motivate and test a new adversarial attack algorithm that measures input perturbation size in a relative componentwise manner. The algorithm can be implemented by solving a sequence of linearly-constrained linear least-squares problems, for which high quality software is available. In the image classification context, as a special case the algorithm may be applied to artificial neural networks that classify printed or handwritten text—we show that it is possible to generate hard-to-spot perturbations that cause misclassification by perturbing only the “ink” and hence leaving the background intact. Such examples are relevant to application areas in defence, business, law and finance.