11 citations · 14 across the 4 of their papers we have counts for
4 papers
Detecting Adversarial Faces Using Only Real Face Self-Perturbations
Qian Wang, Yongqin Xian, Hefei Ling +5
Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when ap…
Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
Vibashan VS, Ning Yu, Chen Xing +5
Existing instance segmentation models learn task-specific information using manual mask annotations from base (training) categories. These mask annotations require tremendous human…
RelaxLoss: Defending Membership Inference Attacks without Losing Utility
Dingfan Chen, Ning Yu, Mario Fritz
As a long-term threat to the privacy of training data, membership inference attacks (MIAs) emerge ubiquitously in machine learning models. Existing works evidence strong connection…
RepMix: Representation Mixing for Robust Attribution of Synthesized Images
Tu Bui, Ning Yu, John Collomosse
Rapid advances in Generative Adversarial Networks (GANs) raise new challenges for image attribution; detecting whether an image is synthetic and, if so, determining which GAN archi…