­Background Deep neural networks have been shown to be vulnerable to adversarial attacks, which pose major security risks in applications such as surveillance, autonomous driving, and access control. Typical attack strategies alter authentic data subtly so as to obtain adversarial samples that resemble the original while jeopardizing network integrity,...