Research Progress of Detection and Defense Methods for Adversarial Examples in Images
Deep neural networks have achieved remarkable success in fields such as image recognition.However,their vul-nerability to adversarial attacks poses a significant threat to the security and reliability of these models.To address this challenge,researchers have proposed numerous methods for the detection and defense against adversarial examples in images.This study cat-egorizes the existing methods into three main categories:detection methods,defense methods,and combined detection and defense methods.Additionally,a detailed subclassification is provided from the perspective of domain classification.The principles,advan-tages,and limitations of these methods are analyzed to offer a comprehensive technical overview for researchers in related fields.Finally,the current challenges in the field of adversarial example detection and defense are summarized,and specific sug-gestions and prospects are proposed in areas such as the construction of cross-domain detection and defense configuration and the introduction of automation technologies.
deep neural networksimage recognitionadversarial example detection and defense
万方数据
维普
