Adversarial Attacks Beyond the Image Space

Understand

Generating adversarial examples is an intriguing problem and an important way of understanding the working mechanism of deep neural networks.

• Most existing approaches generated perturbations in the image space, i.e., each pixel can be modified independently.
• However, in this paper we pay special attention to the subset of adversarial examples that correspond to meaningful changes in 3D physical properties (like rotation and translation, illumination condition, etc.).
• These adversaries arguably pose a more serious concern, as they demonstrate the possibility of causing neural network failure by easy perturbations of real-world 3D objects and scenes.