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Securing Voice Authentication Applications Against Targeted Data Poisoning

Alireza Mohammadi, Keshav Sood, Asef Nazari, Dhananjay Thiruvady

TL;DR

This work proposes a novel defense framework that integrates a regularized convolutional neural network with a K-nearest neighbors classifier, enhanced with stratified cross-validation and class weighting to counteract data imbalance inherent in targeted data poisoning attacks.

Abstract

Deep neural network-based voice authentication systems are promising biometric verification techniques that uniquely identify biological characteristics to verify a user. However, they are particularly susceptible to targeted data poisoning attacks, where attackers replace legitimate users' utterances with their own. We propose an enhanced framework using realworld datasets considering realistic attack scenarios. The results show that the proposed approach is robust, providing accurate authentications even when only a small fraction (5% of the dataset) is poisoned.

Securing Voice Authentication Applications Against Targeted Data Poisoning

TL;DR

This work proposes a novel defense framework that integrates a regularized convolutional neural network with a K-nearest neighbors classifier, enhanced with stratified cross-validation and class weighting to counteract data imbalance inherent in targeted data poisoning attacks.

Abstract

Deep neural network-based voice authentication systems are promising biometric verification techniques that uniquely identify biological characteristics to verify a user. However, they are particularly susceptible to targeted data poisoning attacks, where attackers replace legitimate users' utterances with their own. We propose an enhanced framework using realworld datasets considering realistic attack scenarios. The results show that the proposed approach is robust, providing accurate authentications even when only a small fraction (5% of the dataset) is poisoned.
Paper Structure (9 sections, 1 equation, 3 figures, 3 tables)

This paper contains 9 sections, 1 equation, 3 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. PROPOSED APPROACH
  4. Threat Model
  5. Attack & Defense Design
  6. PERFORMANCE EVALUATION
  7. Performance Evaluation Over User Registration
  8. Performance Evaluation Over Different Attack Scenarios (0.1%, 1%, 5%, 10%)
  9. CONCLUSION

Figures (3)

  • Figure 1: An overview of the authentication process flow, from the initial utterance to the final step of labeling each user as either an attacker or a legitimate user.
  • Figure 2: Heatmap Comparison of the KNN Model Default Implementation Versus Proposed Implementation.
  • Figure 3: Heatmap Comparison of Default Defense Framework Versus Proposed Defense Framework for Different Attack Scenarios.