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20222024
most citedBackdoor Defense via Adaptively Splitting Poisoned Dataset

4 citations · 14 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV20241 cited

Video Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMs

Jinmin Li, Kuofeng Gao, Yang Bai +2

The advent of video-based Large Language Models (LLMs) has significantly enhanced video understanding. However, it has also raised some safety concerns regarding data protection, a…

cs.CV20242 cited

Energy-Latency Manipulation of Multi-modal Large Language Models via Verbose Samples

Kuofeng Gao, Jindong Gu, Yang Bai +4

Despite the exceptional performance of multi-modal large language models (MLLMs), their deployment requires substantial computational resources. Once malicious users induce high en…

cs.CV20241 cited

FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs

Jinmin Li, Kuofeng Gao, Yang Bai +3

Despite the remarkable performance of video-based large language models (LLMs), their adversarial threat remains unexplored. To fill this gap, we propose the first adversarial atta…

cs.CV20244 cited

Inducing High Energy-Latency of Large Vision-Language Models with Verbose Images

Kuofeng Gao, Yang Bai, Jindong Gu +4

Large vision-language models (VLMs) such as GPT-4 have achieved exceptional performance across various multi-modal tasks. However, the deployment of VLMs necessitates substantial e…

cs.CV20234 cited

Backdoor Defense via Adaptively Splitting Poisoned Dataset

Kuofeng Gao, Yang Bai, Jindong Gu +2

Backdoor defenses have been studied to alleviate the threat of deep neural networks (DNNs) being backdoor attacked and thus maliciously altered. Since DNNs usually adopt some exter…

cs.CV20221 cited

Imperceptible and Robust Backdoor Attack in 3D Point Cloud

Kuofeng Gao, Jiawang Bai, Baoyuan Wu +2

With the thriving of deep learning in processing point cloud data, recent works show that backdoor attacks pose a severe security threat to 3D vision applications. The attacker inj…