96 citations · 100 across the 8 of their papers we have counts for
9 papers
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…
Prediction Inconsistency Helps Achieve Generalizable Detection of Adversarial Examples
Sicong Han, Chenhao Lin, Zhengyu Zhao +6
Adversarial detection protects models from adversarial attacks by refusing suspicious test samples. However, current detection methods often suffer from weak generalization: their…
A Survey on Adversarial Machine Learning for Code Data: Realistic Threats, Countermeasures, and Interpretations
Yulong Yang, Haoran Fan, Chenhao Lin +4
Code Language Models (CLMs) have achieved tremendous progress in source code understanding and generation, leading to a significant increase in research interests focused on applyi…
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving
Junhao Zheng, Chenhao Lin, Jiahao Sun +3
Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks. Previous physical attacks against…
LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection
Jiwei Tian, Chao Shen, Buhong Wang +4
Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on de…
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…
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…