Enhancing transferability of black-box adversarial attacks via lifelong learning for speech emotion recognition models

Zhao Ren, Jing Han, Nicholas Cummins, Björn W. Schuller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Well-designed adversarial examples can easily fool deep speech emotion recognition models into misclassifications. The transferability of adversarial attacks is a crucial evaluation indicator when generating adversarial examples to fool a new target model or multiple models. Herein, we propose a method to improve the transferability of black-box adversarial attacks using lifelong learning. First, black-box adversarial examples are generated by an atrous Convolutional Neural Network (CNN) model. This initial model is trained to attack a CNN target model. Then, we adapt the trained atrous CNN attacker to a new CNN target model using lifelong learning. We use this paradigm, as it enables multi-task sequential learning, which saves more memory space than conventional multi-task learning. We verify this property on an emotional speech database, by demonstrating that the updated atrous CNN model can attack all target models which have been learnt, and can better attack a new target model than an attack model trained on one target model only.

Original languageEnglish
Title of host publicationInterspeech 2020
PublisherInternational Speech Communication Association
Pages496-500
Number of pages5
ISBN (Print)9781713820697
DOIs
StatePublished - 2020
Externally publishedYes
Event21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China
Duration: 25 Oct 202029 Oct 2020

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2020-October
ISSN (Print)2308-457X
ISSN (Electronic)1990-9772

Conference

Conference21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020
Country/TerritoryChina
CityShanghai
Period25/10/2029/10/20

Keywords

  • Black-box Adversarial Attacks
  • Lifelong Learning
  • Speech Emotion Recognition
  • Transferability

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