With the rapidly increasing popularity of deep neural networks for image recognition tasks, a parallel interest in generating adversarial examples to attack the trained models has arisen. To date, these approaches have involved either directly computing gradients with respect to the image pixels or directly solving an optimization on the image pixels. We generalize this pursuit in a novel direction: can a separate network be trained to efficiently attack another fully trained network? We demonstrate that it is possible, and that the generated attacks yield startling insights into the weaknesses of the target network. We call such a network an Adversarial Transformation Network (ATN). ATNs transform any input into an adversarial attack on the target network, while being minimally perturbing to the original inputs and the target network's outputs. Further, we show that ATNs are capable of not only causing the target network to make an error, but can be constructed to explicitly control the type of misclassification made. We demonstrate ATNs on both simple MNIST-digit classifiers and state-of-the-art ImageNet classifiers deployed by Google, Inc.: Inception ResNet-v2.

We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.