3 citations · 9 across the 15 of their papers we have counts for
6 papers · 1 filter
Image Can Bring Your Memory Back: A Novel Multi-Modal Guided Attack against Image Generation Model Unlearning
Renyang Liu, Guanlin Li, Tianwei Zhang +1
Recent advances in image generation models (IGMs), particularly diffusion-based architectures such as Stable Diffusion (SD), have markedly enhanced the quality and diversity of AI-…
Semi-LLIE: Semi-supervised Contrastive Learning with Mamba-based Low-light Image Enhancement
Guanlin Li, Ke Zhang, Ting Wang +3
Despite the impressive advancements made in recent low-light image enhancement techniques, the scarcity of paired data has emerged as a significant obstacle to further advancements…
PRIME: Protect Your Videos From Malicious Editing
Guanlin Li, Shuai Yang, Jie Zhang +1
With the development of generative models, the quality of generated content keeps increasing. Recently, open-source models have made it surprisingly easy to manipulate and edit pho…
Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness
Guanlin Li, Naishan Zheng, Man Zhou +2
Adversarial examples are one of the most severe threats to deep learning models. Numerous works have been proposed to study and defend adversarial examples. However, these works la…
Warfare:Breaking the Watermark Protection of AI-Generated Content
Guanlin Li, Yifei Chen, Jie Zhang +5
AI-Generated Content (AIGC) is rapidly expanding, with services using advanced generative models to create realistic images and fluent text. Regulating such content is crucial to p…
A Benchmark of Long-tailed Instance Segmentation with Noisy Labels
Guanlin Li, Guowen Xu, Tianwei Zhang
In this paper, we consider the instance segmentation task on a long-tailed dataset, which contains label noise, i.e., some of the annotations are incorrect. There are two main reas…