Feature Extraction Based on Denoising Auto Encoder for Classification of Adversarial Examples

Yuma Yamasaki, Minoru Kuribayashi, Nobuo Funabiki, Huy H. Nguyen, Isao Echizen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Adversarial examples have been recognized as one of the threats to machine learning techniques. Tiny perturbations are added to multimedia content to cause a misclassification in a target CNN - based model. In conventional studies, such perturbations are removed using a couple of filters, and for classification, the features are extracted from the observations of the output of the CNN-based model. However, the use of well-known filters may enable an attacker to adjust an adversarial attack to deal with such filters and fool the detector. In this study, we investigated the effectiveness of certain auto encoders (AEs) in extracting the traces of perturbations. Even if the structure of the AE is leaked, the difference in the training datasets makes an adjustment of the attack difficult to achieve. The effectiveness of the AE designed in this study was evaluated experimentally, and its combination with some known filters was also evaluated.

Original languageEnglish
Title of host publication2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1815-1820
Number of pages6
ISBN (Electronic)9789881476890
Publication statusPublished - 2021
Event2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, Japan
Duration: Dec 14 2021Dec 17 2021

Publication series

Name2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

Conference

Conference2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Country/TerritoryJapan
CityTokyo
Period12/14/2112/17/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Instrumentation

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