A lightweight deep learning model for rapid detection of fabricated ENF signals from audio sources

Adilet Pazylkarim; Deeraj Nagothu; Yu Chen

doi:10.1117/12.3013456

6 June 2024 A lightweight deep learning model for rapid detection of fabricated ENF signals from audio sources

Adilet Pazylkarim, Deeraj Nagothu, Yu Chen

Proceedings Volume 13058, Disruptive Technologies in Information Sciences VIII; 1305811 (2024) https://doi.org/10.1117/12.3013456
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

The rapid advancement of multimedia content editing software tools has made it increasingly easy for malicious actors to manipulate real-time multimedia data streams, encompassing audio and video. Among the notorious cybercrimes, replay attacks have gained widespread prevalence, necessitating the development of more efficient authentication methods for detection. A cutting-edge authentication technique leverages Electrical Network Frequency (ENF) signals embedded within multimedia content. ENF signals offer a range of advantageous attributes, including uniqueness, unpredictability, and total randomness, rendering them highly effective for detecting replay attacks. To counter potential attackers who may seek to deceive detection systems by embedding fake ENF signals, this study harnesses the growing accessibility of deep Convolutional Neural Networks (CNNs). These CNNs are not only deployable on platforms with limited computational resources, such as Single-Board Computers (SBCs), but they also exhibit the capacity to swiftly identify interference within a signal by learning distinctive spatio-temporal patterns. In this paper, we explore applying a Computationally Efficient Deep Learning Model (CEDM) as a powerful tool for rapidly detecting potential fabrications within ENF signals originating from diverse audio sources. Our experimental study validates the effectiveness of the proposed method.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Adilet Pazylkarim, Deeraj Nagothu, and Yu Chen "A lightweight deep learning model for rapid detection of fabricated ENF signals from audio sources", Proc. SPIE 13058, Disruptive Technologies in Information Sciences VIII, 1305811 (6 June 2024); https://doi.org/10.1117/12.3013456

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