InfoARD: Enhancing Adversarial Robustness Distillation With Attack-Strength Adaptation and Mutual-Information...

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision

Background:

• Deep neural networks are vulnerable to adversarial attacks.
• Existing adversarial distillation (AD) methods lack robustness due to fixed attack strength and attention shifts.

Purpose of the Study:

• To introduce InfoARD, a novel adversarial distillation paradigm.
• To enhance adversarial robustness in deep learning models against attacks and perturbations.

Main Methods:

• Implemented Attack-Strength Adaptation (ASA) for dynamic attack strength adjustment.
• Utilized Mutual-Information Maximization (MIM) for deeper student model understanding.
• Employed multi-granularity distillation for comprehensive knowledge transfer.

Main Results:

• InfoARD demonstrates superior adversarial robustness compared to state-of-the-art methods.
• Experiments on challenging datasets validate the effectiveness of InfoARD.
• The proposed method achieves smoother and generalized classification boundaries.

Conclusions:

• InfoARD significantly boosts adversarial robustness in deep learning models.
• The method is seamlessly integrable into existing AD frameworks.
• InfoARD offers a promising direction for developing more secure AI systems.