3 citations · 11 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…