Scholarlink

Deep Neural Networks (DNNs) perform excellently on most problems, although the issue with many complex models is their vulnerability to adversarial attacks, which can mislead the model’s explanation. Recent work indicates that explanation mechanisms can be compromised by an attacker who alters the explanation while the output remains accurate. This lowers the reliability and robustness of the explanation. Our work considers the threat of such an attack to Explainable Artificial Intelligence (xAI) and introduces a mechanism for defending the explanation against such attacks. Our research proposes NODA (Normalization Defense Against Adversaries), based on a Hessian regularizer and data normalization, to enhance the reliability of the explanation. Our investigation validates that NODA is effective in defending against the attack while not damaging the model’s performance.

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

Defensive Strategy for Explainability in Deep Neural Networks Under Adversarial Attacks

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Tuan Trung Mac,
  • Tan Loc Nguyen,
  • Bac Le

摘要

Deep Neural Networks (DNNs) perform excellently on most problems, although the issue with many complex models is their vulnerability to adversarial attacks, which can mislead the model’s explanation. Recent work indicates that explanation mechanisms can be compromised by an attacker who alters the explanation while the output remains accurate. This lowers the reliability and robustness of the explanation. Our work considers the threat of such an attack to Explainable Artificial Intelligence (xAI) and introduces a mechanism for defending the explanation against such attacks. Our research proposes NODA (Normalization Defense Against Adversaries), based on a Hessian regularizer and data normalization, to enhance the reliability of the explanation. Our investigation validates that NODA is effective in defending against the attack while not damaging the model’s performance.