Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks

Understand

Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications.

• However, they are shown to be vulnerable to adversarial example attack, which generates malicious output by adding slight perturbations to the input.
• Previous adversarial example crafting methods, however, use simple metrics to evaluate the distances between the original examples and the adversarial ones, which could be easily detected by human eyes.
• In addition, these attacks are often not robust due to the inevitable noises and deviation in the physical world.