Effective Universal Unrestricted Adversarial Attacks using a MOE Approach

• URL: http://arxiv.org/abs/2103.00250v1
• Date: Sat, 27 Feb 2021 15:43:06 GMT
• Title: Effective Universal Unrestricted Adversarial Attacks using a MOE Approach
• Abstract summary: We present a multi-objective nested evolutionary algorithm to generate universal adversarial examples in a black-box scenario. The unrestricted attacks are performed through the application of well-known image filters. Experimental results showed that this approach is able to create a sequence of filters capable of generating very effective and undetectable attacks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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