10 papers
The Platonic Defense: Backdoor Defense for Self-Supervised Encoders in the Era of Large Scale Pre-training
Tuo Chen, Minjing Dong, Benlei Cui +2
Self-supervised learning (SSL) pretrained models have become a dominant paradigm for visual representation learning, but they are vulnerable to backdoor attacks. Existing defenses…
HDRFace: Rethinking Face Restoration with High-Dimensional Representation
Zirui Wang, Xianhui Lin, Yi Dong +7
Face restoration under complex degradations still remains an ill-posed inverse problem due to severe information loss. Although diffusion models benefit from strong generative prio…
Revisiting Adversarial Training under Hyperspectral Image
Weihua Zhang, Chengze Jiang, Minjing Dong +5
Recent studies have shown that deep learning-based hyperspectral image (HSI) classification models are highly vulnerable to adversarial attacks, posing significant security risks.…
Diversifying Counterattacks: Orthogonal Exploration for Robust CLIP Inference
Chengze Jiang, Minjing Dong, Xinli Shi +1
Vision-language pre-training models (VLPs) demonstrate strong multimodal understanding and zero-shot generalization, yet remain vulnerable to adversarial examples, raising concerns…
Backdooring Self-Supervised Contrastive Learning by Noisy Alignment
Tuo Chen, Jie Gui, Minjing Dong +3
Self-supervised contrastive learning (CL) effectively learns transferable representations from unlabeled data containing images or image-text pairs but suffers vulnerability to dat…
A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions
Shuxian Zhao, Jie Gui, Minjing Dong +5
The small sample imbalance (S&I) problem is a major challenge in machine learning and data analysis. It is characterized by a small number of samples and an imbalanced class distri…