4.5 Article

Revisiting model's uncertainty and confidences for adversarial example detection

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。
Review Computer Science, Artificial Intelligence

Adversarial example detection for DNN models: a review and experimental comparison

Ahmed Aldahdooh et al.

Summary: Deep learning has achieved great success in many human-related tasks and has been widely adopted in computer vision applications. Defense and detection of adversarial examples pose challenges in safety-critical applications. Existing literature proposes various countermeasures, but there is limited focus on detection methods for adversarial examples. This paper provides a survey on test-time evasion attack detection methods for neural network classifiers in image classification tasks, including experimental results of eight state-of-the-art detectors on four datasets.

ARTIFICIAL INTELLIGENCE REVIEW (2022)

Article Computer Science, Artificial Intelligence

Adversarial example detection based on saliency map features

Shen Wang et al.

Summary: In recent years, machine learning has significantly enhanced image recognition capabilities, but has also revealed vulnerabilities in neural network models to adversarial examples. By utilizing interpretability methods to reveal internal decision-making behaviors of models, researchers were able to propose an effective method for detecting adversarial examples based on multilayer saliency features. Experimental results demonstrated the method's capability to effectively detect adversarial examples across various attack scenarios, comparable to state-of-the-art methods.

APPLIED INTELLIGENCE (2022)

Proceedings Paper Computer Science, Artificial Intelligence

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)

Proceedings Paper Computer Science, Artificial Intelligence

Auxiliary Training: Towards Accurate and Robust Models

Linfeng Zhang et al.

2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)

Proceedings Paper Computer Science, Artificial Intelligence

Defending Against Universal Attacks Through Selective Feature Regeneration

Tejas Borkar et al.

2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)

Article Computer Science, Artificial Intelligence

Image Super-Resolution as a Defense Against Adversarial Attacks

Aamir Mustafa et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2020)

Review Automation & Control Systems

Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

Han Xu et al.

INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING (2020)

Article Computer Science, Information Systems

Combining PRNU and noiseprint for robust and efficient device source identification

Davide Cozzolino et al.

EURASIP JOURNAL ON INFORMATION SECURITY (2020)

Article Computer Science, Information Systems

Anomalous Example Detection in Deep Learning: A Survey

Saikiran Bulusu et al.

IEEE ACCESS (2020)

Article Computer Science, Artificial Intelligence

One Pixel Attack for Fooling Deep Neural Networks

Jiawei Su et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)

Article Computer Science, Information Systems

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

Naveed Akhtar et al.

IEEE ACCESS (2018)

Proceedings Paper Computer Science, Theory & Methods

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)

Proceedings Paper Computer Science, Artificial Intelligence

Deflecting Adversarial Attacks with Pixel Deflection

Aaditya Prakash et al.

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)

Proceedings Paper Computer Science, Information Systems

Towards Evaluating the Robustness of Neural Networks

Nicholas Carlini et al.

2017 IEEE SYMPOSIUM ON SECURITY AND PRIVACY (SP) (2017)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Proceedings Paper Computer Science, Artificial Intelligence

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)

Article Mathematics, Applied

Interior-point methods

FA Potra et al.

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS (2000)