Scholarlink

Abstract <p>A novel type of attack against perceptron-based neural networks with piecewise linear activation functions using basic linear algebra is proposed. The attack is formulated as a system of linear equations and inequalities and demonstrates a simplified and computationally efficient approach to generating diverse sets of adversarial samples. The proposed attack algorithms are implemented in code available in an open-source repository. The study highlights the serious challenge that the proposed attack methodology poses to modern neural network defense emphasizing the urgent need for innovative defense strategies. By comprehensively exploring adversarial vulnerabilities, this study contributes to improving the adversarial robustness of machine learning thus paving way for developing more robust and trustworthy AI systems in real-world applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SLAP — Simple Linear Attack against Perceptron (SLAP)

  • 期刊论文

  • 发表日期：

    2025年11月5日

查看全文
  • A. I. Perminov

摘要

Abstract

A novel type of attack against perceptron-based neural networks with piecewise linear activation functions using basic linear algebra is proposed. The attack is formulated as a system of linear equations and inequalities and demonstrates a simplified and computationally efficient approach to generating diverse sets of adversarial samples. The proposed attack algorithms are implemented in code available in an open-source repository. The study highlights the serious challenge that the proposed attack methodology poses to modern neural network defense emphasizing the urgent need for innovative defense strategies. By comprehensively exploring adversarial vulnerabilities, this study contributes to improving the adversarial robustness of machine learning thus paving way for developing more robust and trustworthy AI systems in real-world applications.