13 citations · 13 across the 8 of their papers we have counts for
8 papers
IP Protection in the Era of Visual Generative AI: A Survey
Zhuan Shi, Shunchang Liu, Alireza Dehghanpour Farashah +8
The rapid evolution of visual generative AI has introduced a wide range of intellectual property risks, spanning the unauthorized learning, reproduction, extraction, misuse, and re…
Preference Instability in Reward Models: Detection and Mitigation via Sparse Autoencoders
Shunchang Liu, Xin Chen, Belen Martin Urcelay +1
Preference learning in large language models relies on reward models as proxies for human judgment. However, these models frequently exhibit preference instability, producing contr…
The Biased Oracle: Assessing LLMs' Understandability and Empathy in Medical Diagnoses
Jianzhou Yao, Shunchang Liu, Guillaume Drui +3
Large language models (LLMs) show promise for supporting clinicians in diagnostic communication by generating explanations and guidance for patients. Yet their ability to produce o…
CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models
Shunchang Liu, Zhuan Shi, Lingjuan Lyu +2
Assessing whether AI-generated images are substantially similar to source works is a crucial step in resolving copyright disputes. In this paper, we propose CopyJudge, a novel auto…
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations
Tony Ma, Songze Li, Yisong Xiao +1
The transferability of adversarial examples is a crucial aspect of evaluating the robustness of deep learning systems, particularly in black-box scenarios. Although several methods…
Benchmarking the Robustness of Quantized Models
Yisong Xiao, Tianyuan Zhang, Shunchang Liu +1
Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities wh…
Harnessing Perceptual Adversarial Patches for Crowd Counting
Shunchang Liu, Jiakai Wang, Aishan Liu +4
Crowd counting, which has been widely adopted for estimating the number of people in safety-critical scenes, is shown to be vulnerable to adversarial examples in the physical world…
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World
Jiakai Wang, Aishan Liu, Zixin Yin +3
Deep learning models are vulnerable to adversarial examples. As a more threatening type for practical deep learning systems, physical adversarial examples have received extensive r…