15 papers
Erase the Mark, Preserve the Motion: Temporally Consistent Removal of Video Watermarks
Yifan Liao, Zhen Sun, Zhicong Huang +6
Recent advances in video generation have increased the demand for reliable watermarking for copyright protection, yet the robustness of video watermarks against removal attacks rem…
ARENA: Automated Red-Teaming for Large Audio Language Models
Jiaming He, Zhicong Huang, Tian Jin +5
Large audio-language models (LALMs) make it possible to interact with language models through speech, music, and environmental sound, but they also introduce a safety surface that…
Leak-Resistant Unlearning: A New Benchmark for Evaluating Multi-Hop Reasoning Consistency and Recovery Robustness
Haoting Qian, Qingjie Zhang, Zhicong Huang +2
Benchmarking machine unlearning methods is critical to understand whether sensitive knowledge is removed from large language models (LLMs) or not. Current unlearning benchmarks inc…
When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer
Zhen Sun, Yifan Liao, Zhicong Huang +4
Machine-generated text (MGT) attribution aims to identify the specific generator responsible for a given text, thereby providing fine-grained evidence for model accountability and…
Robustness of Vision Foundation Models to Common Perturbations
Hongbin Liu, Zhengyuan Jiang, Cheng Hong +1
A vision foundation model outputs an embedding vector for an image, which can be affected by common editing operations (e.g., JPEG compression, brightness, contrast adjustments). T…
VLMShield: Efficient and Robust Defense of Vision-Language Models against Malicious Prompts
Peigui Qi, Kunsheng Tang, Yanpu Yu +7
Vision-Language Models (VLMs) face significant safety vulnerabilities from malicious prompt attacks due to weakened alignment during visual integration. Existing defenses suffer fr…
Hidden Ads: Behavior Triggered Semantic Backdoors for Advertisement Injection in Vision Language Models
Duanyi Yao, Changyue Li, Zhicong Huang +2
Vision-Language Models (VLMs) are increasingly deployed in consumer applications where users seek recommendations about products, dining, and services. We introduce Hidden Ads, a n…
FIT to Forget: Robust Continual Unlearning for Large Language Models
Xiaoyu Xu, Minxin Du, Kun Fang +5
While large language models (LLMs) exhibit remarkable capabilities, they increasingly face demands to unlearn memorized privacy-sensitive, copyrighted, or harmful content. Existing…
Turn-Based Structural Triggers: Structure-Conditioned Backdoors in Multi-Turn LLMs
Yiyang Lu, Jinwen He, Yue Zhao +4
Large Language Models (LLMs) are increasingly deployed as multi-turn assistants and customized through instruction tuning with project-specific training components. This practice c…
EnchTable: Unified Safety Alignment Transfer in Fine-tuned Large Language Models
Jialin Wu, Kecen Li, Zhicong Huang +3
Many machine learning models are fine-tuned from large language models (LLMs) to achieve high performance in specialized domains like code generation, biomedical analysis, and math…
Understanding the Dilemma of Unlearning for Large Language Models
Qingjie Zhang, Haoting Qian, Zhicong Huang +5
Unlearning seeks to remove specific knowledge from large language models (LLMs), but its effectiveness remains contested. On one side, "forgotten" knowledge can often be recovered…
MARS: A Malignity-Aware Backdoor Defense in Federated Learning
Wei Wan, Yuxuan Ning, Zhicong Huang +7
Federated Learning (FL) is a distributed paradigm aimed at protecting participant data privacy by exchanging model parameters to achieve high-quality model training. However, this…
Fingerprinting LLMs via Prompt Injection
Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li +4
Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization, which makes it challenging to determine whether one mod…
GaussMarker: Robust Dual-Domain Watermark for Diffusion Models
Kecen Li, Zhicong Huang, Xinwen Hou +1
As Diffusion Models (DM) generate increasingly realistic images, related issues such as copyright and misuse have become a growing concern. Watermarking is one of the promising sol…
VideoMarkBench: Benchmarking Robustness of Video Watermarking
Zhengyuan Jiang, Moyang Guo, Kecen Li +5
The rapid development of video generative models has led to a surge in highly realistic synthetic videos, raising ethical concerns related to disinformation and copyright infringem…