3 citations · 15 across the 21 of their papers we have counts for
15 papers · 1 filter
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…
Adversarial Backdoor Defense in CLIP
Junhao Kuang, Siyuan Liang, Jiawei Liang +2
Multimodal contrastive pretraining, exemplified by models like CLIP, has been found to be vulnerable to backdoor attacks. While current backdoor defense methods primarily employ co…
Revisiting Backdoor Attacks against Large Vision-Language Models from Domain Shift
Siyuan Liang, Jiawei Liang, Tianyu Pang +5
Instruction tuning enhances large vision-language models (LVLMs) but increases their vulnerability to backdoor attacks due to their open design. Unlike prior studies in static sett…
Object Detectors in the Open Environment: Challenges, Solutions, and Outlook
Siyuan Liang, Wei Wang, Ruoyu Chen +5
With the emergence of foundation models, deep learning-based object detectors have shown practical usability in closed set scenarios. However, for real-world tasks, object detector…
Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning
Siyuan Liang, Kuanrong Liu, Jiajun Gong +4
Multimodal contrastive learning has emerged as a powerful paradigm for building high-quality features using the complementary strengths of various data modalities. However, the ope…
VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models
Jiawei Liang, Siyuan Liang, Man Luo +4
Autoregressive Visual Language Models (VLMs) showcase impressive few-shot learning capabilities in a multimodal context. Recently, multimodal instruction tuning has been proposed t…
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection
Jiawei Liang, Siyuan Liang, Aishan Liu +3
The proliferation of face forgery techniques has raised significant concerns within society, thereby motivating the development of face forgery detection methods. These methods aim…
Pre-trained Trojan Attacks for Visual Recognition
Aishan Liu, Xinwei Zhang, Yisong Xiao +6
Pre-trained vision models (PVMs) have become a dominant component due to their exceptional performance when fine-tuned for downstream tasks. However, the presence of backdoors with…
BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning
Siyuan Liang, Mingli Zhu, Aishan Liu +3
Studying backdoor attacks is valuable for model copyright protection and enhancing defenses. While existing backdoor attacks have successfully infected multimodal contrastive learn…