Latest

• Group-based Robustness: A General Framework for Customized Robustness in the Real World
• Adversarial Training for Raw-Binary Malware Classifiers
• Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
• Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
• $n$-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers
• A General Framework for Adversarial Examples with Objectives
• On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples
• Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition