In recent years, the success of deep learning has carried over from discriminative models to generative models. In particular, generative adversarial networks (GANs) have facilitated a new level of performance ranging from media manipulation to dataset re-generation. Despite the success, the potential risks of privacy breach stemming from GANs are less well explored. In this paper, we focus on membership inference attack against GANs that has the potential to reveal information about victim models' training data. Specifically, we present the first taxonomy of membership inference attacks, which encompasses not only existing attacks but also our novel ones. We also propose the first generic attack model that can be instantiated in various settings according to adversary's knowledge about the victim model. We complement our systematic analysis of attack vectors with a comprehensive experimental study, that investigates the effectiveness of these attacks w.r.t. model type, training configurations, and attack type across three diverse application scenarios ranging from images, over medical data to location data. We show consistent effectiveness in all the setups, which bridges the assumption gap and performance gap in previous study with a complete spectrum of performance across settings. We conclusively remind users to think over before publicizing any part of their models.

Recent advances in Generative Adversarial Networks (GANs) have shown increasing success in generating photorealistic images. But they also raise challenges to visual forensics and model authentication. We present the first study of learning GAN fingerprints towards image attribution: we systematically investigate the performance of classifying an image as real or GAN-generated. For GAN-generated images, we further identify their sources. Our experiments validate that GANs carry distinct model fingerprints and leave stable fingerprints to their generated images, which support image attribution. Even a single difference in GAN training initialization can result in different fingerprints, which enables fine-grained model authentication. We further validate such a fingerprint is omnipresent in different image components and is not biased by GAN artifacts. Fingerprint finetuning is effective in immunizing five types of adversarial image perturbations. Comparisons also show our learned fingerprints consistently outperform several baselines in a variety of setups.