263 citations · 767 across the 31 of their papers we have counts for
9 papers · 1 filter
AdvFAS: A robust face anti-spoofing framework against adversarial examples
Jiawei Chen, Xiao Yang, Heng Yin +6
Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques. Despite considerable advancements in…
Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization
Zhaoxia Yin, Shaowei Zhu, Hang Su +3
Deep Neural Networks (DNNs) have recently made significant progress in many fields. However, studies have shown that DNNs are vulnerable to adversarial examples, where imperceptibl…
Adversarial Example Defense via Perturbation Grading Strategy
Shaowei Zhu, Wanli Lyu, Bin Li +2
Deep Neural Networks have been widely used in many fields. However, studies have shown that DNNs are easily attacked by adversarial examples, which have tiny perturbations and grea…
Neural network fragile watermarking with no model performance degradation
Zhaoxia Yin, Heng Yin, Xinpeng Zhang
Deep neural networks are vulnerable to malicious fine-tuning attacks such as data poisoning and backdoor attacks. Therefore, in recent research, it is proposed how to detect malici…
Universal adversarial perturbation for remote sensing images
Qingyu Wang, Guorui Feng, Zhaoxia Yin +1
Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditi…
Reversible Attack based on Local Visual Adversarial Perturbation
Li Chen, Shaowei Zhu, Zhaoxia Yin
Adding perturbations to images can mislead classification models to produce incorrect results. Recently, researchers exploited adversarial perturbations to protect image privacy fr…
PICA: A Pixel Correlation-based Attentional Black-box Adversarial Attack
Jie Wang, Zhaoxia Yin, Jin Tang +2
The studies on black-box adversarial attacks have become increasingly prevalent due to the intractable acquisition of the structural knowledge of deep neural networks (DNNs). Howev…
Attention-Guided Black-box Adversarial Attacks with Large-Scale Multiobjective Evolutionary Optimization
Jie Wang, Zhaoxia Yin, Jing Jiang +1
Fooling deep neural networks (DNNs) with the black-box optimization has become a popular adversarial attack fashion, as the structural prior knowledge of DNNs is always unknown. Ne…
An Efficient Pre-processing Method to Eliminate Adversarial Effects
Hua Wang, Jie Wang, Zhaoxia Yin
Deep Neural Networks (DNNs) are vulnerable to adversarial examples generated by imposing subtle perturbations to inputs that lead a model to predict incorrect outputs. Currently, a…