Generative Adversarial Perturbations


We propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool trained models. Our approach 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 both classification and semantic segmentation models, obviating the need for hand-crafting attack methods for each task. We improve the state-of-the-art performance in universal perturbations by leveraging generative models in lieu of current iterative methods. Our attacks are considerably faster than iterative and optimization-based methods at inference time. Moreover, we are the first to present effective targeted universal perturbations.