In:
網際網路技術學刊, Angle Publishing Co., Ltd., Vol. 25, No. 1 ( 2024-01), p. 147-155
Abstract:
〈 p 〉 With the widespread application of deep neural networks in image detection, adversarial sample attacks have gradually become a hot issue of concern for researchers. In this paper we propose a new adversarial sample detection approach called AdvDetector, which combines image generation through label fusion with image similarity detection. AdvDetector enhances sample quality and effectively identifies adversarial samples. Specifically, the method generates images by selecting seed pixels, the labels of deep neural network classification, and the pixel distribution learned from training data, and detects them using image similarity comparison methods. During the sample generation process, we introduce the AdvDetector method for adversarial sample detection to improve the quality of generated samples. We evaluated the effectiveness of the method on three publicly available image datasets, MNIST, Cifar-10, and GTSR, and the results show that the method is superior to existing baseline methods in terms of adversarial sample detection rate and sample generation quality. 〈 /p 〉 〈 p 〉 & nbsp; 〈 /p 〉
Type of Medium:
Online Resource
ISSN:
1607-9264
,
1607-9264
Uniform Title:
Research on Adversarial Sample Detection Method Based on Image Similarity
DOI:
10.53106/160792642024012501012
Language:
Unknown
Publisher:
Angle Publishing Co., Ltd.
Publication Date:
2024
detail.hit.zdb_id:
2464390-7
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