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20212026
most citedHard Adversarial Example Mining for Improving Robust Fairness

1 citations · 1 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR2026

MacPrompt: Maraconic-guided Jailbreak against Text-to-Image Models

Xi Ye, Yiwen Liu, Lina Wang +4

Text-to-image (T2I) models have raised increasing safety concerns due to their capacity to generate NSFW and other banned objects. To mitigate these risks, safety filters and conce…

cs.CR2024

SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection

Qiannan Wang, Changchun Yin, Lu Zhou +1

The extensive adoption of Self-supervised learning(SSL) has led to an increased security threat from backdoor attacks. While existing research has mainly focused on backdoor attack…

cs.CV2023

GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning

Qiannan Wang, Changchun Yin, Zhe Liu +3

Within the realm of computer vision, self-supervised learning (SSL) pertains to training pre-trained image encoders utilizing a substantial quantity of unlabeled images. Pre-traine…

cs.LG2023★ 1 cited

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…

cs.CR2023

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…

cs.CR2021

Fingerprinting Image-to-Image Generative Adversarial Networks

Guanlin Li, Guowen Xu, Han Qiu +5

Generative Adversarial Networks (GANs) have been widely used in various application scenarios. Since the production of a commercial GAN requires substantial computational and human…