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20232026
most citedA Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication

2 citations · 2 across the 22 of their papers we have counts for

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6 papers · 1 filter

cs.CR2025

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…

cs.CR2025

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…

cs.CR2025

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…

cs.CR2024

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…

cs.CR2024

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…

cs.CR2024

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…