A Statistical Method for Attack-Agnostic Adversarial Attack Detection with Compressive Sensing Comparison

• URL: http://arxiv.org/abs/2510.02707v1
• Date: Fri, 03 Oct 2025 04:05:20 GMT
• Title: A Statistical Method for Attack-Agnostic Adversarial Attack Detection with Compressive Sensing Comparison
• Authors: Chinthana Wimalasuriya, Spyros Tragoudas,
• Abstract summary: We propose a statistical approach that establishes a detection baseline before a neural network's deployment.<n>We generate a metric of adversarial presence by comparing the behavior of a compressed/uncompressed neural network pair.<n>Our method has been tested against state-of-the-art techniques, and achieves it near-perfect detection across a wide range of attack types.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Adversarial attacks present a significant threat to modern machine learning systems. Yet, existing detection methods often lack the ability to detect unseen attacks or detect different attack types with a high level of accuracy. In this work, we propose a statistical approach that establishes a detection baseline before a neural network's deployment, enabling effective real-time adversarial detection. We generate a metric of adversarial presence by comparing the behavior of a compressed/uncompressed neural network pair. Our method has been tested against state-of-the-art techniques, and it achieves near-perfect detection across a wide range of attack types. Moreover, it significantly reduces false positives, making it both reliable and practical for real-world applications.

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