Overview of Adversarial Defenses on Image Classification

This short post provides an overview of adversarial defense techniques in image classification. It is intended as a brief summary for readers interested in robustness against adversarial attacks.

The included PDF summarizes five major categories:

• Adversarial Training: Incorporating adversarial examples during training to improve robustness.
• Gradient Masking / Regularization: Obscuring gradient information to hinder attack generation.
• Detection-based Defenses: Identifying adversarial inputs before classification.
• Transformation-based Defenses: Applying input transformations (e.g., denoising, compression) to remove perturbations.
• Certified Defenses: Providing provable robustness guarantees against bounded perturbations.

📄 Download slides overviewing adversarial defenses here.