13 citations · 69 across the 12 of their papers we have counts for
6 papers · 1 filter
Feature Noise Boosts DNN Generalization under Label Noise
Lu Zeng, Xuan Chen, Xiaoshuang Shi +1
The presence of label noise in the training data has a profound impact on the generalization of deep neural networks (DNNs). In this study, we introduce and theoretically demonstra…
Exposing the Fake: Effective Diffusion-Generated Images Detection
Ruipeng Ma, Jinhao Duan, Fei Kong +2
Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffus…
Improve Video Representation with Temporal Adversarial Augmentation
Jinhao Duan, Quanfu Fan, Hao Cheng +2
Recent works reveal that adversarial augmentation benefits the generalization of neural networks (NNs) if used in an appropriate manner. In this paper, we introduce Temporal Advers…
An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization
Fei Kong, Jinhao Duan, RuiPeng Ma +4
Recently, diffusion models have achieved remarkable success in generating tasks, including image and audio generation. However, like other generative models, diffusion models are p…
Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation
Jin Sun, Xiaoshuang Shi, Zhiyuan Wang +3
Modeling in Computer Vision has evolved to MLPs. Vision MLPs naturally lack local modeling capability, to which the simplest treatment is combined with convolutional layers. Convol…
Are Diffusion Models Vulnerable to Membership Inference Attacks?
Jinhao Duan, Fei Kong, Shiqi Wang +2
Diffusion-based generative models have shown great potential for image synthesis, but there is a lack of research on the security and privacy risks they may pose. In this paper, we…