Detection Defense Against Adversarial Attacks with Saliency Map

Understand

It is well established that neural networks are vulnerable to adversarial examples, which are almost imperceptible on human vision and can cause the deep models misbehave.

• Such phenomenon may lead to severely inestimable consequences in the safety and security critical applications.
• Existing defenses are trend to harden the robustness of models against adversarial attacks, e.g., adversarial training technology.
• However, these are usually intractable to implement due to the high cost of re-training and the cumbersome operations of altering the model architecture or parameters.