3 citations · 3 across the 5 of their papers we have counts for
7 papers
Adaptive Co-Serving LLM Watermarking on Modern Inference Engines
Kieu Dang, Phung Lai, Ching-Yun Ko +1
Large language model (LLM) watermarking is important for ownership verification and intellectual property protection. However, existing approaches focus on algorithmic design while…
Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection
Ching-Hao Chiu, Hao-Wei Chung, Gelei Xu +7
With the rapid adoption of generative AI, synthetic medical images pose growing risks, including diagnostic deception and insurance fraud. Although prior work has explored vision-l…
STAR: Spectral Truncation and Rescale for Model Merging
Yu-Ang Lee, Ching-Yun Ko, Tejaswini Pedapati +3
Model merging is an efficient way of obtaining a multi-task model from several pretrained models without further fine-tuning, and it has gained attention in various domains, includ…
Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models
Chung-Ting Tsai, Ching-Yun Ko, I-Hsin Chung +2
The rapid advancement of generative models has introduced serious risks, including deepfake techniques for facial synthesis and editing. Traditional approaches rely on training cla…
Attention Tracker: Detecting Prompt Injection Attacks in LLMs
Kuo-Han Hung, Ching-Yun Ko, Ambrish Rawat +3
Large Language Models (LLMs) have revolutionized various domains but remain vulnerable to prompt injection attacks, where malicious inputs manipulate the model into ignoring origin…
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness
Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung +1
As deep neural networks (DNNs) are increasingly deployed in sensitive applications, ensuring their security and robustness has become critical. A major threat to DNNs arises from a…
Steal Now and Attack Later: Evaluating Robustness of Object Detection against Black-box Adversarial Attacks
Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung +1
Latency attacks against object detection represent a variant of adversarial attacks that aim to inflate the inference time by generating additional ghost objects in a target image.…