58 citations · 116 across the 12 of their papers we have counts for
12 papers
Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations
Haitong Liu, Kuofeng Gao, Yang Bai +4
Recently, video-based large language models (video-based LLMs) have achieved impressive performance across various video comprehension tasks. However, this rapid advancement raises…
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…
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…
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…
BackdoorBox: A Python Toolbox for Backdoor Learning
Yiming Li, Mengxi Ya, Yang Bai +2
Third-party resources (, samples, backbones, and pre-trained models) are usually involved in the training of deep neural networks (DNNs), which brings backdoor attacks as a n…
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
Yiming Li, Yang Bai, Yong Jiang +3
Deep neural networks (DNNs) have demonstrated their superiority in practice. Arguably, the rapid development of DNNs is largely benefited from high-quality (open-sourced) datasets,…