10 citations · 15 across the 7 of their papers we have counts for
7 papers
GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection
Yan Ju, Shan Jia, Jialing Cai +2
With the rapid development of deep generative models (such as Generative Adversarial Networks and Diffusion models), AI-synthesized images are now of such high quality that humans…
Unsupervised Visual Representation Learning by Synchronous Momentum Grouping
Bo Pang, Yifan Zhang, Yaoyi Li +2
In this paper, we propose a genuine group-level contrastive visual representation learning method whose linear evaluation performance on ImageNet surpasses the vanilla supervised l…
Multi-scale Graph Convolutional Networks with Self-Attention
Zhilong Xiong, Jia Cai
Graph convolutional networks (GCNs) have achieved remarkable learning ability for dealing with various graph structural data recently. In general, deep GCNs do not work well since…
SStaGCN: Simplified stacking based graph convolutional networks
Jia Cai, Zhilong Xiong, Shaogao Lv
Graph convolutional network (GCN) is a powerful model studied broadly in various graph structural data learning tasks. However, to mitigate the over-smoothing phenomenon, and deal…
Towards Robust Neural Networks via Orthogonal Diversity
Kun Fang, Qinghua Tao, Yingwen Wu +5
Deep Neural Networks (DNNs) are vulnerable to invisible perturbations on the images generated by adversarial attacks, which raises researches on the adversarial robustness of DNNs.…
Type I Attack for Generative Models
Chengjin Sun, Sizhe Chen, Jia Cai +1
Generative models are popular tools with a wide range of applications. Nevertheless, it is as vulnerable to adversarial samples as classifiers. The existing attack methods mainly f…
HRFA: High-Resolution Feature-based Attack
Zhixing Ye, Sizhe Chen, Peidong Zhang +2
Adversarial attacks have long been developed for revealing the vulnerability of Deep Neural Networks (DNNs) by adding imperceptible perturbations to the input. Most methods generat…