96 citations · 144 across the 19 of their papers we have counts for
20 papers
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…
A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication
Jingyi Deng, Chenhao Lin, Zhengyu Zhao +4
Deep generative models have demonstrated impressive performance in various computer vision applications, including image synthesis, video generation, and medical analysis. Despite…
Systematic Categorization, Construction and Evaluation of New Attacks against Multi-modal Mobile GUI Agents
Yulong Yang, Xinshan Yang, Shuaidong Li +4
The integration of Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) into mobile GUI agents has significantly enhanced user efficiency and experience. Howe…
Deep Learning Library Testing: Definition, Methods and Challenges
Xiaoyu Zhang, Weipeng Jiang, Chao Shen +4
In recent years, software systems powered by deep learning (DL) techniques have significantly facilitated people's lives in many aspects. As the backbone of these DL systems, vario…
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…
Adversarial Example Soups: Improving Transferability and Stealthiness for Free
Bo Yang, Hengwei Zhang, Jindong Wang +4
Transferable adversarial examples cause practical security risks since they can mislead a target model without knowing its internal knowledge. A conventional recipe for maximizing…