5 citations · 12 across the 21 of their papers we have counts for
22 papers
HiRoute: Hierarchical Routed Prompt Tuning for Safety Alignment of Large Language Models
Fangzhou Chen, Shiji Zhao, Mengyang Wang +4
Large language models (LLMs) remain vulnerable to harmful requests and jailbreak attacks. Parameter-efficient safety alignment methods based on prompt tuning typically rely on a si…
Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding
Yisong Xiao, Aishan Liu, Yongxin Huang +6
Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from their training data, resultin…
A Multimodal Automatic Redteaming Evaluation based on Atomic Jailbreak Strategy Decoupling and Combination
Shiji Zhao, Yuxuan Zhou, Chen Xiong +3
Multimodal Large Language Models (MLLMs) have achieved impressive progress in image-text comprehension and generation, yet they remain susceptible to jailbreak attacks that can tri…
VQ-Jarvis: Retrieval-Augmented Video Restoration Agent with Sharp Vision and Fast Thought
Xuanyu Zhang, Weiqi Li, Qunliang Xing +6
Video restoration in real-world scenarios is challenged by heterogeneous degradations, where static architectures and fixed inference pipelines often fail to generalize. Recent age…
Improving Safety Alignment via Balanced Direct Preference Optimization
Shiji Zhao, Mengyang Wang, Shukun Xiong +7
With the rapid development and widespread application of Large Language Models (LLMs), their potential safety risks have attracted widespread attention. Reinforcement Learning from…
Mind over Space: Can Multimodal Large Language Models Mentally Navigate?
Qihui Zhu, Shouwei Ruan, Xiao Yang +6
Despite the widespread adoption of MLLMs in embodied agents, their capabilities remain largely confined to reactive planning from immediate observations, consistently failing in sp…
VRSA: Jailbreaking Multimodal Large Language Models through Visual Reasoning Sequential Attack
Shiji Zhao, Shukun Xiong, Yao Huang +7
Multimodal Large Language Models (MLLMs) are widely used in various fields due to their powerful cross-modal comprehension and generation capabilities. However, more modalities bri…
Oyster-I: Beyond Refusal -- Constructive Safety Alignment for Responsible Language Models
Ranjie Duan, Jiexi Liu, Xiaojun Jia +27
Large language models (LLMs) typically deploy safety mechanisms to prevent harmful content generation. Most current approaches focus narrowly on risks posed by malicious actors, of…
Strata-Sword: A Hierarchical Safety Evaluation towards LLMs based on Reasoning Complexity of Jailbreak Instructions
Shiji Zhao, Ranjie Duan, Jiexi Liu +10
Large language models (LLMs) have gained widespread recognition for their superior comprehension and have been deployed across numerous domains. Building on Chain-of-Thought (CoT)…
Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective
Maoxun Yuan, Duanni Meng, Ziteng Xi +4
Infrared small target detection and segmentation (IRSTDS) is a critical yet challenging task in defense and civilian applications, owing to the dim, shapeless appearance of targets…
Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label Distillation
Shiji Zhao, Chi Chen, Ranjie Duan +2
Adversarial Training (AT) is widely recognized as an effective approach to enhance the adversarial robustness of Deep Neural Networks. As a variant of AT, Adversarial Robustness Di…
MoAPT: Mixture of Adversarial Prompt Tuning for Vision-Language Models
Shiji Zhao, Qihui Zhu, Shukun Xiong +7
Large pre-trained Vision Language Models (VLMs) demonstrate excellent generalization capabilities but remain highly susceptible to adversarial examples, posing potential security r…
The Path to Reconciling Quality and Safety in Text-to-Image Generation: Dataset, Method, and Evaluation
Shouwei Ruan, Zhenyu Wu, Yao Huang +5
Content safety is a fundamental challenge for text-to-image (T2I) models, yet prevailing methods enforce a debilitating trade-off between safety and generation quality. We argue th…
When Lighting Deceives: Exposing Vision-Language Models' Illumination Vulnerability Through Illumination Transformation Attack
Hanqing Liu, Shouwei Ruan, Yao Huang +2
Vision-Language Models (VLMs) have achieved remarkable success in various tasks, yet their robustness to real-world illumination variations remains largely unexplored. To bridge th…
Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency
Shiji Zhao, Ranjie Duan, Fengxiang Wang +7
Multimodal Large Language Models (MLLMs) have achieved impressive performance and have been put into practical use in commercial applications, but they still have potential safety…
OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations
Caixin Kang, Yubo Chen, Shouwei Ruan +5
With the rise of deep learning, facial recognition technology has seen extensive research and rapid development. Although facial recognition is considered a mature technology, we f…
Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation
Shiji Zhao, Ranjie Duan, Xizhe Wang +1
Adversarial Training (AT) has been widely proved to be an effective method to improve the adversarial robustness against adversarial examples for Deep Neural Networks (DNNs). As a…
Mitigating Accuracy-Robustness Trade-off via Balanced Multi-Teacher Adversarial Distillation
Shiji Zhao, Xizhe Wang, Xingxing Wei
Adversarial Training is a practical approach for improving the robustness of deep neural networks against adversarial attacks. Although bringing reliable robustness, the performanc…
Boosting Adversarial Transferability with Learnable Patch-wise Masks
Xingxing Wei, Shiji Zhao
Adversarial examples have attracted widespread attention in security-critical applications because of their transferability across different models. Although many methods have been…
Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters
Xingxing Wei, Shiji Zhao, Bo li
Adversarial attacks have been proven to be potential threats to Deep Neural Networks (DNNs), and many methods are proposed to defend against adversarial attacks. However, while enh…
Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking
Xingxing Wei, Bangzheng Pu, Shiji Zhao +2
The advancement of deep learning has facilitated the integration of Artificial Intelligence (AI) into clinical practices, particularly in computer-aided diagnosis. Given the pivota…
Visual Adversarial Attacks and Defenses in the Physical World: A Survey
Xingxing Wei, Bangzheng Pu, Shiji Zhao +2
Although Deep Neural Networks (DNNs) have been widely applied in various real-world scenarios, they remain vulnerable to adversarial examples. Adversarial attacks in computer visio…