SmsNet: A New Deep Convolutional Neural Network Model for Adversarial Example Detection

Document Type

Article

Publication Date

1-1-2022

Abstract

The emergence of adversarial examples has had a significant impact on the development and application of deep learning. In this paper, a novel convolutional neural network model, the stochastic multifilter statistical network (SmsNet), is proposed for the detection of adversarial examples. A feature statistical layer is constructed to collect statistical data of feature map output from each convolutional layer in SmsNet by combining manual features with a neural network. The entire model is an end-to-end detection model, so the feature statistical layer is not independent of the network, and its output is directly transmitted to the fully connected layer by a short-cut connection called the SmsConnection. Additionally, a dynamic pruning strategy is introduced to simplify the model structure for better performance. The experiments demonstrate the effectiveness of the network structure and pruning strategy, and the proposed model achieves high detection rates against state-of-the-art adversarial attacks.

Identifier

85099598500 (Scopus)

Publication Title

IEEE Transactions on Multimedia

External Full Text Location

https://doi.org/10.1109/TMM.2021.3050057

e-ISSN

19410077

ISSN

15209210

First Page

230

Last Page

244

Volume

24

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