Fine-grained Synthesis of Unrestricted Adversarial Examples

Understand

We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation.

• Unlike existing unrestricted attacks that typically hand-craft geometric transformations, we learn stylistic and stochastic modifications leveraging state-of-the-art generative models.
• This allows us to manipulate an image in a controlled, fine-grained manner without being bounded by a norm threshold.
• Our approach can be used for targeted and non-targeted unrestricted attacks on classification, semantic segmentation and object detection models.