Adversarial Robustness on Image Classification with $k$-means

• URL: http://arxiv.org/abs/2312.09533v2
• Date: Tue, 13 Feb 2024 01:53:13 GMT
• Title: Adversarial Robustness on Image Classification with $k$-means
• Authors: Rollin Omari, Junae Kim and Paul Montague
• Abstract summary: We evaluate the vulnerability of $k$-means clustering algorithms to adversarial attacks, emphasising the associated security risks. We introduce and evaluate an adversarial training method that improves testing performance in adversarial scenarios.
• Score: 3.5385056709199536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we explore the challenges and strategies for enhancing the robustness of $k$-means clustering algorithms against adversarial manipulations. We evaluate the vulnerability of clustering algorithms to adversarial attacks, emphasising the associated security risks. Our study investigates the impact of incremental attack strength on training, introduces the concept of transferability between supervised and unsupervised models, and highlights the sensitivity of unsupervised models to sample distributions. We additionally introduce and evaluate an adversarial training method that improves testing performance in adversarial scenarios, and we highlight the importance of various parameters in the proposed training method, such as continuous learning, centroid initialisation, and adversarial step-count.

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