2 citations · 2 across the 6 of their papers we have counts for
7 papers
Forensic-Aware Continual Adaptation for Image Forgery Localization
Chenqi Kong, Song Xia, Anwei Luo +3
The rapid evolution of image manipulation techniques has raised growing public security concerns. Existing Image Forgery Localization (IFL) methods can accurately localize manipula…
Certified vs. Empirical Adversarial Robust-ness via Hybrid Convolutions with Attention Stochasticity
Joy Dhar, Song Xia, Manish Kumar Pandey +5
We introduce Hybrid Convolutions with Attention Stochasticity (HyCAS), an adversarial defense that narrows the long-standing gap between provable robustness under L2 certificates a…
MTL-UE: Learning to Learn Nothing for Multi-Task Learning
Yi Yu, Song Xia, Siyuan Yang +5
Most existing unlearnable strategies focus on preventing unauthorized users from training single-task learning (STL) models with personal data. Nevertheless, the paradigm has recen…
Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation
Yi Yu, Song Xia, Xun Lin +5
Adversarial examples, characterized by imperceptible perturbations, pose significant threats to deep neural networks by misleading their predictions. A critical aspect of these exa…
Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Trigger
Yi Yu, Song Xia, Xun Lin +4
No-Reference Image Quality Assessment (NR-IQA), responsible for assessing the quality of a single input image without using any reference, plays a critical role in evaluating and o…
Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders
Yi Yu, Yufei Wang, Song Xia +4
Unlearnable examples (UEs) seek to maximize testing error by making subtle modifications to training examples that are correctly labeled. Defenses against these poisoning attacks c…
Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations
Xun Lin, Yi Yu, Song Xia +8
The widespread availability of publicly accessible medical images has significantly propelled advancements in various research and clinical fields. Nonetheless, concerns regarding…