相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Adversarial example detection for DNN models: a review and experimental comparison
Ahmed Aldahdooh et al.
ARTIFICIAL INTELLIGENCE REVIEW (2022)
Adversarial example detection based on saliency map features
Shen Wang et al.
APPLIED INTELLIGENCE (2022)
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework
Xuanqing Liu et al.
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)
Auxiliary Training: Towards Accurate and Robust Models
Linfeng Zhang et al.
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)
Defending Against Universal Attacks Through Selective Feature Regeneration
Tejas Borkar et al.
2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)
Image Super-Resolution as a Defense Against Adversarial Attacks
Aamir Mustafa et al.
IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
Han Xu et al.
INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING (2020)
Combining PRNU and noiseprint for robust and efficient device source identification
Davide Cozzolino et al.
EURASIP JOURNAL ON INFORMATION SECURITY (2020)
Anomalous Example Detection in Deep Learning: A Survey
Saikiran Bulusu et al.
IEEE ACCESS (2020)
One Pixel Attack for Fooling Deep Neural Networks
Jiawei Su et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Naveed Akhtar et al.
IEEE ACCESS (2018)
LEMNA: Explaining Deep Learning based Security Applications
Wenbo Guo et al.
PROCEEDINGS OF THE 2018 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (CCS'18) (2018)
Deflecting Adversarial Attacks with Pixel Deflection
Aaditya Prakash et al.
2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini et al.
2017 IEEE SYMPOSIUM ON SECURITY AND PRIVACY (SP) (2017)
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Nguyen et al.
2015 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2015)
Interior-point methods
FA Potra et al.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS (2000)