5 citations · 8 across the 22 of their papers we have counts for
8 papers · 1 filter
Privacy on the Fly: A Predictive Adversarial Transformation Network for Mobile Sensor Data
Tianle Song, Chenhao Lin, Yang Cao +5
Mobile motion sensors such as accelerometers and gyroscopes are now ubiquitously accessible by third-party apps via standard APIs. While enabling rich functionalities like activity…
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…
Revisiting Training-Inference Trigger Intensity in Backdoor Attacks
Chenhao Lin, Chenyang Zhao, Shiwei Wang +3
Backdoor attacks typically place a specific trigger on certain training data, such that the model makes prediction errors on inputs with that trigger during inference. Despite the…
Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration Path
Yuchen Ren, Zhengyu Zhao, Chenhao Lin +4
Transferable adversarial examples are known to cause threats in practical, black-box attack scenarios. A notable approach to improving transferability is using integrated gradients…
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…
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…
Revisiting Transferable Adversarial Images: Systemization, Evaluation, and New Insights
Zhengyu Zhao, Hanwei Zhang, Renjue Li +6
Transferable adversarial images raise critical security concerns for computer vision systems in real-world, black-box attack scenarios. Although many transfer attacks have been pro…
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…