Recent studies have shown that Deep Leaning models are susceptible to adversarial examples, which are data, in general images, intentionally modified to fool a machine learning classifier. In this paper, we present a multi-objective nested evolutionary algorithm to generate universal unrestricted adversarial examples in a black-box scenario. The unrestricted attacks are performed through the application of well-known image filters that are available in several image processing libraries, modern cameras, and mobile applications. The multi-objective optimization takes into account not only the attack success rate but also the detection rate. Experimental results showed that this approach is able to create a sequence of filters capable of generating very effective and undetectable attacks.
@article{arxiv.2103.00250,
title = {Effective Universal Unrestricted Adversarial Attacks using a MOE Approach},
author = {A. E. Baia and G. Di Bari and V. Poggioni},
journal= {arXiv preprint arXiv:2103.00250},
year = {2026}
}