Abstract
Adversarial attacks pose significant challenges to the robustness of machine learning models. This paper explores the one-pixel attacks in image classification, a black-box adversarial attack that introduces changes to the pixels of the input images to make the classifier predict erroneously. We use a pragmatic approach by employing different evolutionary algorithms - Differential Evolution, Genetic Algorithms, and Covariance Matrix Adaptation Evolution Strategy - to find and optimise these one-pixel attacks. We focus on understanding how these algorithms generate effective one-pixel attacks. The experimentation was carried out on the CIFAR-10 dataset, a widespread benchmark in image classification. The experimental results cover an analysis of the following aspects: fitness optimisation, number of evaluations to generate an adversarial attack, success rate, number of adversarial attacks found per image, solution space coverage and level of distortion done to the original image to generate the attack. Overall, the experimentation provided insights into the nuances of the one-pixel attack and compared three standard evolutionary algorithms, showcasing each algorithm’s potential and evolutionary computation’s ability to find solutions in this strict case of the adversarial attack.
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Acknowledgements
This work has been partially supported by Project “NEXUS Pacto de Inovação - Transição Verde e Digital para Transportes, Logística e Mobilidade”. ref. No. 7113, supported by the Recovery and Resilience Plan (PRR) and by the European Funds Next Generation EU, following Notice No. 02/C05-i01/2022.PC645112083-00000059 (project 53), Component 5 - Capitalization and Business Innovation - Mobilizing Agendas for Business Innovation; by Project No. 7059 - Neuraspace - AI fights Space Debris, reference C644877546-00000020, supported by the RRP - Recovery and Resilience Plan and the European Next Generation EU Funds, following Notice No. 02/C05-i01/2022, Component 5 - Capitalization and Business Innovation - Mobilizing Agendas for Business Innovation and; by the FCT - Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R &D Unit - UIDB/00326/2020 or project code UIDP/00326/2020.
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Clare, L., Marques, A., Correia, J. (2024). A Comparative Analysis of Evolutionary Adversarial One-Pixel Attacks. In: Smith, S., Correia, J., Cintrano, C. (eds) Applications of Evolutionary Computation. EvoApplications 2024. Lecture Notes in Computer Science, vol 14635. Springer, Cham. https://doi.org/10.1007/978-3-031-56855-8_9
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