Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective

Understand

Deep Learning algorithms have achieved the state-of-the-art performance for Image Classification and have been used even in security-critical applications, such as biometric recognition systems and self-driving cars.

• However, recent works have shown those algorithms, which can even surpass the human capabilities, are vulnerable to adversarial examples.
• In Computer Vision, adversarial examples are images containing subtle perturbations generated by malicious optimization algorithms in order to fool classifiers.
• As an attempt to mitigate these vulnerabilities, numerous countermeasures have been constantly proposed in literature.