Black-Box Training Data Identification in GANs via Detector Networks
Lukman Olagoke Salil Vadhan Seth Neel
Since their inception Generative Adversarial Networks (GANs) have been
popular generative models across images, audio, video, and tabular data. In
this paper we study whether given access to a trained GAN, as well as fresh
samples from the underlying distribution, if it is possible for an attacker to
efficiently identify if a given point is a member of the GAN's training data.
This is of interest for both reasons related to copyright, where a user may
want to determine if their copyrighted data has been used to train a GAN, and
in the study of data privacy, where the ability to detect training set
membership is known as a membership inference attack. Unlike the majority of
prior work this paper investigates the privacy implications of using GANs in
black-box settings, where the attack only has access to samples from the
generator, rather than access to the discriminator as well. We introduce a
suite of membership inference attacks against GANs in the black-box setting and
evaluate our attacks on image GANs trained on the CIFAR10 dataset and tabular
GANs trained on genomic data. Our most successful attack, called The Detector,
involve training a second network to score samples based on their likelihood of
being generated by the GAN, as opposed to a fresh sample from the distribution.
We prove under a simple model of the generator that the detector is an
approximately optimal membership inference attack. Across a wide range of
tabular and image datasets, attacks, and GAN architectures, we find that
adversaries can orchestrate non-trivial privacy attacks when provided with
access to samples from the generator. At the same time, the attack success
achievable against GANs still appears to be lower compared to other generative
and discriminative models; this leaves the intriguing open question of whether
GANs are in fact more private, or if it is a matter of developing stronger
attacks.