Deep neural networks (DNNs) are vulnerable to adversarial examples, which are
crafted by adding imperceptible perturbations to inputs. Recently different
attacks and strategies have been proposed, but how to generate adversarial
examples perceptually realistic and more efficiently remains unsolved. This
paper proposes a novel framework called Attack-Inspired GAN (AI-GAN), where a
generator, a discriminator, and an attacker are trained jointly. Once trained,
it can generate adversarial perturbations efficiently given input images and
target classes. Through extensive experiments on several popular datasets eg
MNIST and CIFAR-10, AI-GAN achieves high attack success rates and reduces
generation time significantly in various settings. Moreover, for the first
time, AI-GAN successfully scales to complicated datasets eg CIFAR-100 with
around $90%$ success rates among all classes.

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