3 citations · 9 across the 11 of their papers we have counts for
11 papers
Latent Geometric Chords for Query-Efficient Decision-Based Adversarial Attacks
Ei Hmue Khine, Yao Li, Jiebao Sun +3
While decision-based black-box adversarial attacks present a severe security threat, current methodologies suffer from fundamental limitations. Pixel-wise attacks frequently introd…
PDAC: Efficient Coreset Selection for Continual Learning via Probability Density Awareness
Junqi Gao, Zhichang Guo, Dazhi Zhang +3
Rehearsal-based Continual Learning (CL) maintains a limited memory buffer to store replay samples for knowledge retention, making these approaches heavily reliant on the quality of…
SAR-FAH: A Frequency-Adaptive Hybrid Network based on Neural ODEs for Structural-Preserving SAR Despeckling
Ziqing Ma, Chang Yang, Zhichang Guo +1
Synthetic Aperture Radar (SAR) images are inherently degraded by speckle noise that severely limits their reliability in high-precision applications. As a signal-dependent multipli…
Progressive Alignment Degradation Learning for Pansharpening
Enzhe Zhao, Zhichang Guo, Yao Li +2
Deep learning-based pansharpening has been shown to effectively generate high-resolution multispectral (HRMS) images. To create supervised ground-truth HRMS images, synthetic data…
A Tunable Despeckling Neural Network Stabilized via Diffusion Equation
Yi Ran, Zhichang Guo, Jia Li +3
The removal of multiplicative Gamma noise is a critical research area in the application of synthetic aperture radar (SAR) imaging, where neural networks serve as a potent tool. Ho…
Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling
Jie Ning, Jiebao Sun, Shengzhu Shi +4
Deep learning-based image denoising models demonstrate remarkable performance, but their lack of robustness analysis remains a significant concern. A major issue is that these mode…
Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability
Junqi Gao, Biqing Qi, Yao Li +4
The transferability of adversarial perturbations provides an effective shortcut for black-box attacks. Targeted perturbations have greater practicality but are more difficult to tr…
Variable-order fractional 1-Laplacian diffusion equations for multiplicative noise removal
Yuhang Li, Zhichang Guo, Jingfeng Shao +2
In this paper, we study a class of fractional -Laplacian diffusion equations with variable orders, proposed as a model for multiplicative noise removal. The existence and unique…
PINNsFailureRegion Localization and Refinement through White-box AdversarialAttack
Shengzhu Shi, Yao Li, Zhichang Guo +2
Physics-informed neural networks (PINNs) have shown great promise in solving partial differential equations (PDEs). However, vanilla PINNs often face challenges when solving comple…
A Review of Adversarial Attacks in Computer Vision
Yutong Zhang, Yao Li, Yin Li +1
Deep neural networks have been widely used in various downstream tasks, especially those safety-critical scenario such as autonomous driving, but deep networks are often threatened…
Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer
Enzhe Zhao, Zhichang Guo, Shengzhu Shi +3
In the hyperspectral image classification (HSIC) task, the most commonly used model validation paradigm is partitioning the training-test dataset through pixel-wise random sampling…