2 citations · 4 across the 14 of their papers we have counts for
15 papers
R-PGA: Robust Physical Adversarial Camouflage Generation via Relightable 3D Gaussian Splatting
Tianrui Lou, Siyuan Liang, Jiawei Liang +2
Physical adversarial camouflage poses a severe security threat to autonomous driving systems by mapping adversarial textures onto 3D objects. Nevertheless, current methods remain b…
Bridging the Task Gap: Multi-Task Adversarial Transferability in CLIP and Its Derivatives
Kuanrong Liu, Siyuan Liang, Cheng Qian +2
As a general-purpose vision-language pretraining model, CLIP demonstrates strong generalization ability in image-text alignment tasks and has been widely adopted in downstream appl…
Text Adversarial Attacks with Dynamic Outputs
Wenqiang Wang, Siyuan Liang, Xiao Yan +1
Text adversarial attack methods are typically designed for static scenarios with fixed numbers of output labels and a predefined label space, relying on extensive querying of the v…
Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation
Ruoyu Chen, Xiaoqing Guo, Kangwei Liu +6
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in aligning visual inputs with natural language outputs. Yet, the extent to which generated token…
Evidence Recomposition and Predictive Context Residualization for Visual Attribution in Multimodal Large Language Models
Jiawei Liang, Jianjie Huang, Xianghao Jiao +3
Multimodal large language models (MLLMs) have achieved strong vision-language performance, yet their token-level visual evidence remains difficult to inspect. Recent logit-lens att…
3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation
Tianrui Lou, Xiaojun Jia, Siyuan Liang +4
Physical adversarial attack methods expose the vulnerabilities of deep neural networks and pose a significant threat to safety-critical scenarios such as autonomous driving. Camouf…
Physical Adversarial Camouflage through Gradient Calibration and Regularization
Jiawei Liang, Siyuan Liang, Jianjie Huang +3
The advancement of deep object detectors has greatly affected safety-critical fields like autonomous driving. However, physical adversarial camouflage poses a significant security…
SMA: Who Said That? Auditing Membership Leakage in Semi-Black-box RAG Controlling
Shixuan Sun, Siyuan Liang, Jianjie Huang +2
Retrieval-Augmented Generation (RAG) and its Multimodal Retrieval-Augmented Generation (MRAG) significantly improve the knowledge coverage and contextual understanding of Large Lan…
SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents
Siyuan Liang, Tianmeng Fang, Zhe Liu +5
With the wide application of multimodal foundation models in intelligent agent systems, scenarios such as mobile device control, intelligent assistant interaction, and multimodal t…
Robust Anti-Backdoor Instruction Tuning in LVLMs
Yuan Xun, Siyuan Liang, Xiaojun Jia +2
Large visual language models (LVLMs) have demonstrated excellent instruction-following capabilities, yet remain vulnerable to stealthy backdoor attacks when finetuned using contami…
Poison Once, Control Anywhere: Clean-Text Visual Backdoors in VLM-based Mobile Agents
Xuan Wang, Siyuan Liang, Zhe Liu +5
Mobile agents powered by vision-language models (VLMs) are increasingly adopted for tasks such as UI automation and camera-based assistance. These agents are typically fine-tuned u…
No Query, No Access
Wenqiang Wang, Siyuan Liang, Yangshijie Zhang +3
Textual adversarial attacks mislead NLP models, including Large Language Models (LLMs), by subtly modifying text. While effective, existing attacks often require knowledge of the v…
Interpreting Object-level Foundation Models via Visual Precision Search
Ruoyu Chen, Siyuan Liang, Jingzhi Li +5
Advances in multimodal pre-training have propelled object-level foundation models, such as Grounding DINO and Florence-2, in tasks like visual grounding and object detection. Howev…
Efficient Backdoor Defense in Multimodal Contrastive Learning: A Token-Level Unlearning Method for Mitigating Threats
Kuanrong Liu, Siyuan Liang, Jiawei Liang +2
Multimodal contrastive learning uses various data modalities to create high-quality features, but its reliance on extensive data sources on the Internet makes it vulnerable to back…
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack
Mingli Zhu, Siyuan Liang, Baoyuan Wu
Deep neural networks face persistent challenges in defending against backdoor attacks, leading to an ongoing battle between attacks and defenses. While existing backdoor defense st…