Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples ca...Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.展开更多
Remote sensing images exhibit rich texture features and strong autocorrelation.Although the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local informa...Remote sensing images exhibit rich texture features and strong autocorrelation.Although the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local information,the limited perceptual field prevents it from establishing long-distance dependence on global information,leading to the low accuracy of remote sensing image reconstruction.Furthermore,it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational demand.In this study,we propose a lightweight distillation CNN-Transformer SR architecture,named DCTA,for remote sensing SR,addressing the aforementioned issues.Specifically,the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and Transformer.In addition,we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation,aiming to select the informative components for better feature fusion.The extracted low-resolution(LR)feature maps are reorganized through the up-sampling module to obtain high-resolution(HR)feature maps with high accuracy to generate highquality HR remote sensing images.The experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets,including NWPU-RESISC45,Draper,and UC Merced.This is achieved by balancing reconstruction performance and network complexity,resulting in both competitive subjective and objective results.展开更多
基金Supported by the National Natural Science Foundation of China(U1903214,62372339,62371350,61876135)the Ministry of Education Industry University Cooperative Education Project(202102246004,220800006041043,202002142012)the Fundamental Research Funds for the Central Universities(2042023kf1033)。
文摘Recent years have witnessed the ever-increasing performance of Deep Neural Networks(DNNs)in computer vision tasks.However,researchers have identified a potential vulnerability:carefully crafted adversarial examples can easily mislead DNNs into incorrect behavior via the injection of imperceptible modification to the input data.In this survey,we focus on(1)adversarial attack algorithms to generate adversarial examples,(2)adversarial defense techniques to secure DNNs against adversarial examples,and(3)important problems in the realm of adversarial examples beyond attack and defense,including the theoretical explanations,trade-off issues and benign attacks in adversarial examples.Additionally,we draw a brief comparison between recently published surveys on adversarial examples,and identify the future directions for the research of adversarial examples,such as the generalization of methods and the understanding of transferability,that might be solutions to the open problems in this field.
基金supported by National Natural Science Foundation of China[42090012]Guangxi Science and Technology Plan Project(Guike 2021AB30019)+4 种基金Hubei Province Key R\&D Project(2022BAA048)Sichuan Province Key R\&D Project(2022YFN0031,2023YFN0022,2023YFS0381)Zhuhai Industry-University-Research Cooperation Project(ZH22017001210098PWC)Shanxi Provincial Science and Technology Major Special Project(202201150401020)Guangxi Key Laboratory of Spatial Information and Surveying and Mapping Fund Project(21-238-21-01).
文摘Remote sensing images exhibit rich texture features and strong autocorrelation.Although the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local information,the limited perceptual field prevents it from establishing long-distance dependence on global information,leading to the low accuracy of remote sensing image reconstruction.Furthermore,it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational demand.In this study,we propose a lightweight distillation CNN-Transformer SR architecture,named DCTA,for remote sensing SR,addressing the aforementioned issues.Specifically,the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and Transformer.In addition,we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation,aiming to select the informative components for better feature fusion.The extracted low-resolution(LR)feature maps are reorganized through the up-sampling module to obtain high-resolution(HR)feature maps with high accuracy to generate highquality HR remote sensing images.The experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets,including NWPU-RESISC45,Draper,and UC Merced.This is achieved by balancing reconstruction performance and network complexity,resulting in both competitive subjective and objective results.
