Learning to Generate Noise for Multi-Attack Robustness

Understand

Adversarial learning has emerged as one of the successful techniques to circumvent the susceptibility of existing methods against adversarial perturbations.

• However, the majority of existing defense methods are tailored to defend against a single category of adversarial perturbation (e.g.
• $\ell_\infty$-attack).
• In safety-critical applications, this makes these methods extraneous as the attacker can adopt diverse adversaries to deceive the system.