Adversarial attacks and defenses in deep learning

Abstract

The adversarial example is a modified image that is added imperceptible perturbations, which can make deep neural networks decide wrongly. The adversarial examples seriously threaten the availability of the system and bring great security risks to the system. Therefore, the representative adversarial attack methods were analyzed, including white-box attacks and black-box attacks. According to the development status of adversarial attacks and defenses, the relevant domestic and foreign defense strategies in recent years were described, including pre-processing, improving model robustness, malicious detection. Finally, future research directions in the field of adversarial attacks and adversarial defenses were given.

Keywords

• adversarial examples
• adversarial attacks
• adversarial defenses
• deep learning security