Due to their capacity for image generation, GAN models may be considered as a solution for
the use of private data, which enhances their commercial value. However, unlike …

Since their inception Generative Adversarial Networks (GANs) have been popular
generative models across images, audio, video, and tabular data. In this paper we study …

Generative Adversarial Networks (GANs) have made releasing of synthetic images a viable
approach to share data without releasing the original dataset. It has been shown that such …

With breakthroughs in high-resolution image generation, applications for disentangled
generative adversarial networks (GANs) have attracted much attention. At the same time, the …

Generative Adversarial Networks (GANs) have made releasing of synthetic images a viable
approach to share data without releasing the original dataset. It has been shown that such …

Generative adversarial networks (GANs) have made unprecedented performance in image
synthesis and play a key role in various downstream applications of computer vision …

Model extraction attacks aim to duplicate a machine learning model through query access to
a target model. Early studies mainly focus on discriminative models. Despite the success …

The quality of GAN-generated fake images has improved significantly, and recent GAN
approaches, such as StyleGAN, achieve near indistinguishability from real images for the …

To detect GAN generated images, conventional supervised machine learning algorithms
require collecting a large number of real images as well as fake images generated by the …

Generative models are subject to overfitting and thus may potentially leak sensitive
information from the training data. In this work. we investigate the privacy risks that can …