Generative Adversarial Perturbations

Understand

In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models.

• We present trainable deep neural networks for transforming images to adversarial perturbations.
• Our proposed models can produce image-agnostic and image-dependent perturbations for both targeted and non-targeted attacks.
• We also demonstrate that similar architectures can achieve impressive results in fooling classification and semantic segmentation models, obviating the need for hand-crafting attack methods for each task.