Adversarial Sample Detection in Computer Vision:A Survey
With the increase in data volume and improvement in hardware performance,deep learning(DL)has made significant progress in the field of computer vision.However,deep learning models are vulnerable to adversarial samples,causing significant changes in the output.As an effective defense method,adversarial sample detection can prevent adversarial samples from affecting the deep learning model without changing the model structure.This paper organizes the research work on adversarial example de-tection in recent years,analyzes the relationship between adversarial example detection and training data,classifies them according to the characteristics used in the detection method,and systematically and comprehensively introduces adversarial sample detec-tion methods in the field of computer vision.Then,some detection methods that combine cross-domain technologies are introduced in detail,and the experimental configurations for training and evaluating detection methods are statistically analyzed.Finally,some technologies that are expected to be applied to adversarial sample detection are summarized,and future research challenges and development directions are prospected.
Deep learningAdversarial sample attacksAdversarial sample detectionAI securityImage classification
万方数据
维普
