2 citations · 2 across the 1 of their papers we have counts for
4 papers
Pay Less Attention to Function Words for Free Robustness of Vision-Language Models
Qiwei Tian, Chenhao Lin, Zhengyu Zhao +1
To address the trade-off between robustness and performance for robust VLM, we observe that function words could incur vulnerability of VLMs against cross-modal adversarial attacks…
Adversarial Video Promotion Against Text-to-Video Retrieval
Qiwei Tian, Chenhao Lin, Zhengyu Zhao +3
Thanks to the development of cross-modal models, text-to-video retrieval (T2VR) is advancing rapidly, but its robustness remains largely unexamined. Existing attacks against T2VR a…
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…