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DeepLASD countermeasure for logical access audio spoofing.

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This study introduces DeepLASD, an end-to-end deep learning system for detecting voice spoofing attacks in authentication systems. It effectively identifies sophisticated voice conversion and text-to-speech threats using raw audio waveforms.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Signal Processing

Background:

  • Voice-based authentication systems face increasing threats from logical access (LA) spoofing using advanced voice conversion (VC) and text-to-speech (TTS) technologies.
  • Existing methods often rely on handcrafted features, which may not capture the nuances of sophisticated spoofing attacks.

Purpose of the Study:

  • To propose and evaluate DeepLASD, an end-to-end deep learning framework for detecting LA spoofing in voice authentication.
  • To process raw audio waveforms directly, eliminating the need for manual feature engineering.

Main Methods:

  • The DeepLASD model utilizes SincConv for spectral processing and residual convolutional blocks with attention for feature extraction.
  • GeLU activation is incorporated within residual blocks to better distinguish real and spoofed speech.
  • A gated recurrent unit models temporal dynamics for enhanced spoof detection.

Main Results:

  • The proposed method achieved a low Equal Error Rate (EER) and a minimum Tandem Detection Cost Function (t-DCF) of 0.1208 on the ASVspoof 2019 and 2021 datasets.
  • Demonstrated strong generalization capabilities against both VC and TTS spoofing techniques.
  • Showcased notable adaptability to next-generation synthetic speech, despite ongoing challenges.

Conclusions:

  • DeepLASD offers a competent and effective solution for detecting logical access spoofing in voice authentication systems.
  • The end-to-end framework enhances security and detection capabilities, improving the robustness of voice-based biometrics.
  • The research highlights the potential of deep learning for real-time anti-spoofing applications.