Out-domain examples for generative models

The existence of adversarial examples has been demonstrated for a quite large set of deep learning architectures [10, 11, 36]. An adversarial input is a carefully forged data instance that aims at driving the model into an incorrect or unexpected behaviour. Moreover, the adversarial setup requires that those instances must be as much as possible indistinguishable from genuine inputs. In the present work, motivated by the extensive studies carried out on adversarial inputs for discriminative models, we extend the adversarial context into the increasingly popular generative models field. In particular, we focused on the most promising class of architectures, called Generative Adversarial Networks (GANs) [9]. GANs implicitly perform generative modeling of a target data distribution by training a deep neural network architecture. This is composed by two neural networks, a generator and a discriminator that are trained simultaneously in a zero-sum game. In the end, the generator learns a deterministic mapping between a latent representation and an approximation of the target data distribution. What we show with the present work is that a pre-trained generator can be forced to reproduce an arbitrary output if fed by a suitable adversarial input.