96 citations · 144 across the 19 of their papers we have counts for
7 papers · 1 filter
Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
Qiwei Tian, Chenhao Lin, Zhengyu Zhao +2
Adversarial training has achieved substantial performance in defending image retrieval against adversarial examples. However, existing studies in deep metric learning (DML) still s…
Towards Deep Learning Models Resistant to Transfer-based Adversarial Attacks via Data-centric Robust Learning
Yulong Yang, Chenhao Lin, Xiang Ji +5
Transfer-based adversarial attacks raise a severe threat to real-world deep learning systems since they do not require access to target models. Adversarial training (AT), which is…
Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection
Chenhao Lin, Fangbin Yi, Hang Wang +3
Face forgery techniques have emerged as a forefront concern, and numerous detection approaches have been proposed to address this challenge. However, existing methods predominantly…
Hard Adversarial Example Mining for Improving Robust Fairness
Chenhao Lin, Xiang Ji, Yulong Yang +4
Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE). Neverth…
SSL-Auth: An Authentication Framework by Fragile Watermarking for Pre-trained Encoders in Self-supervised Learning
Xiaobei Li, Changchun Yin, Liyue Zhu +4
Self-supervised learning (SSL), a paradigm harnessing unlabeled datasets to train robust encoders, has recently witnessed substantial success. These encoders serve as pivotal featu…
Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization
Yulong Yang, Chenhao Lin, Qian Li +6
Quantized neural networks (QNNs) have received increasing attention in resource-constrained scenarios due to their exceptional generalizability. However, their robustness against r…