Adversarial Robustness in XAI Specifications: Why Explainability Must Be Secure

Key Points

• This research aims to explore the necessity of secure specifications in explainable AI to enhance adversarial robustness.
• Analyzed various explainable AI models for vulnerabilities to adversarial attacks.
• Developed criteria for assessing the security of XAI specifications.
• Conducted experiments to evaluate the robustness of different models against adversarial inputs.
• Identified key vulnerabilities in popular explainable AI frameworks.
• Demonstrated that secure specifications significantly improve adversarial robustness.
• Provided quantifiable metrics showing increased resilience against manipulation.