Machine learning models are typically made available to potential client users via inference
APIs. Model extraction attacks occur when a malicious client uses information gleaned from …

Model extraction attacks (MEAs) enable an attacker to replicate the functionality of a victim
deep neural network (DNN) model by only querying its API service remotely, posing a …

By harnessing the latest advances in deep learning, image-to-image translation
architectures have recently achieved impressive capabilities. Unfortunately, the growing …

Due to the great success of image-to-image (Img2Img) translation GANs, many applications
with ethics issues arise, eg, DeepFake and DeepNude, presenting a challenging problem to …

Although image translation algorithms such as StarGAN, STGAN, StarGAN-v2, etc. based on
the generative adversarial networks, bring enormous convenience to people's work and life …

The Pix2Pix Generative Adversarial Network, or GAN, is an approach to training a deep
convolutional neural network for image-to-image translation tasks. The careful configuration …

Training deep neural networks requires significant computational resources and large
datasets that are often confidential or expensive to collect. As a result, owners tend to protect …

The goal of unsupervised image-to-image translation is to map images from one domain to
another without the ground truth correspondence between the two domains. State-of-art …

Image-to-image translation models are shown to be vulnerable to the Membership Inference
Attack (MIA), in which the adversary's goal is to identify whether a sample is used to train the …

In this work, we develop efficient disruptions of black-box image translation deepfake
generation systems. We are the first to demonstrate black-box deepfake generation …