79 citations · 111 across the 14 of their papers we have counts for
16 papers
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks
Qi Peng, Wenlin Liu, Ruoxi Qin +3
Adversarial attacks are considered the intrinsic vulnerability of CNNs. Defense strategies designed for attacks have been stuck in the adversarial attack-defense arms race, reflect…
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout
Pengfei Xie, Linyuan Wang, Ruoxi Qin +4
Deep neural networks(DNNs) is vulnerable to be attacked by adversarial examples. Black-box attack is the most threatening attack. At present, black-box attack methods mainly adopt…
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model
Ruoxi Qin, Linyuan Wang, Xingyuan Chen +2
Deep neural networks have been shown to suffer from critical vulnerabilities under adversarial attacks. This phenomenon stimulated the creation of different attack and defense stra…
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features
Kai Qiao, Chi Zhang, Jian Chen +3
On basis of functional magnetic resonance imaging (fMRI), researchers are devoted to designing visual encoding models to predict the neuron activity of human in response to present…
BigGAN-based Bayesian reconstruction of natural images from human brain activity
Kai Qiao, Jian Chen, Linyuan Wang +3
In the visual decoding domain, visually reconstructing presented images given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI) is di…
AdvJND: Generating Adversarial Examples with Just Noticeable Difference
Zifei Zhang, Kai Qiao, Lingyun Jiang +2
Compared with traditional machine learning models, deep neural networks perform better, especially in image classification tasks. However, they are vulnerable to adversarial exampl…
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples
Wanting Yu, Hongyi Yu, Lingyun Jiang +2
Adversarial examples reveal the vulnerability and unexplained nature of neural networks. Studying the defense of adversarial examples is of considerable practical importance. Most…
Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization
Kai Qiao, Chi Zhang, Jian Chen +3
Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation. Visual encod…
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense
Lingyun Jiang, Kai Qiao, Ruoxi Qin +4
In image classification of deep learning, adversarial examples where inputs intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect resul…
Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices
Kai Qiao, Jian Chen, Linyuan Wang +4
Recently, visual encoding and decoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation.…
A visual encoding model based on deep neural networks and transfer learning
Chi Zhang, Kai Qiao, Linyuan Wang +4
Background: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve hi…
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain
Chi Zhang, Xiaohan Duan, Linyuan Wang +5
Despite the remarkable similarities between convolutional neural networks (CNN) and the human brain, CNNs still fall behind humans in many visual tasks, indicating that there still…
Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network
Chi Zhang, Kai Qiao, Linyuan Wang +3
In recent years, research on decoding brain activity based on functional magnetic resonance imaging (fMRI) has made remarkable achievements. However, constraint-free natural image…
Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture
Kai Qiao, Chi Zhang, Linyuan Wang +4
In neuroscience, all kinds of computation models were designed to answer the open question of how sensory stimuli are encoded by neurons and conversely, how sensory stimuli can be…
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
Hanming Zhang, Liang Li, Kai Qiao +4
Limited angle problem is a challenging issue in x-ray computed tomography (CT) field. Iterative reconstruction methods that utilize the additional prior can suppress artifacts and…
Equivalence of L0 and L1 Minimizations in Sudoku Problem
Linyuan Wang, Wenkun Zhang, Bin Yan +1
Sudoku puzzles can be formulated and solved as a sparse linear system of equations. This problem is a very useful example for the Compressive Sensing (CS) theoretical study. In thi…