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<p>The emergence of deepfake technology, along with other forms of audio forgeries, raises new concerns for digital security and forensic examination. This paper proposes an effective audio forgery detection framework that utilizes a modified ResNet++ deep learning model with attention mechanisms and advanced preprocessing techniques. The algorithm includes a robust detection method that incorporates multi-scale feature fusion and transformer-based attention to a Mel-spectrogram. The model successfully overcomes challenges posed by intricate forgery patterns and overlapping background noise, demonstrating enhanced performance in noisy conditions. In addition, the inclusion of systematic preprocessing steps increases estimation accuracy, which is critical for real-time processing. The proposed algorithm shows good results, boasting 99.92% training accuracy, 95.45% validation accuracy, 99.52% precision, and a false positive rate of only 1.58%.</p>

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Detecting audio forgery using deep learning techniques with attention mechanisms on ResNet++

  • 期刊论文

  • 发表日期：

    2025年7月18日

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  • Deep Das,
  • Rahul Dixit

摘要

The emergence of deepfake technology, along with other forms of audio forgeries, raises new concerns for digital security and forensic examination. This paper proposes an effective audio forgery detection framework that utilizes a modified ResNet++ deep learning model with attention mechanisms and advanced preprocessing techniques. The algorithm includes a robust detection method that incorporates multi-scale feature fusion and transformer-based attention to a Mel-spectrogram. The model successfully overcomes challenges posed by intricate forgery patterns and overlapping background noise, demonstrating enhanced performance in noisy conditions. In addition, the inclusion of systematic preprocessing steps increases estimation accuracy, which is critical for real-time processing. The proposed algorithm shows good results, boasting 99.92% training accuracy, 95.45% validation accuracy, 99.52% precision, and a false positive rate of only 1.58%.