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O. Derrien, "Detection of Genuine Lossless Audio Files: Application to the MPEG-AAC Codec," J. Audio Eng. Soc., vol. 67, no. 3, pp. 116-123, (2019 March.). doi: https://doi.org/10.17743/jaes.2019.0002 O. Derrien, "Detection of Genuine Lossless Audio Files: Application to the MPEG-AAC Codec," J. Audio Eng. Soc., vol. 67 Issue 3 pp. 116-123, (2019 March.). doi: https://doi.org/10.17743/jaes.2019.0002
Abstract: In this study the author describes an algorithm that can discriminate transcoded from true lossless audio files, i.e. a file that was obtained by directly ripping a genuine audio CD from a file that was decoded from a lossy audio format (e.g. MP3, AAC) and re-encoded with a lossless format. This algorithm should allow both consumers and online music dealers to check the authenticity of lossless music files. Although research focuses on the MPEG AAC codec, the approach can also be applied to the MP3 codec. It is based on the detection of quantization errors in the time-frequency domain without using machine learning. Tests with a large database of 1576 audio files, original and transcoded with AAC from iTunes, show null false positive ratios and very low false negative ratios, possibly null for high-precision settings, which is better than other methods. It was shown that a perfectly accurate classification can be reached, provided a sufficiently long computation time is allowed. Furthermore, the method is naturally immune to truncation of audio files and global gain modification.

@article{derrien2019detection,
author={derrien, olivier},
journal={journal of the audio engineering society},
title={detection of genuine lossless audio files: application to the mpeg-aac codec},
year={2019},
volume={67},
number={3},
pages={116-123},
doi={https://doi.org/10.17743/jaes.2019.0002},
month={march},} @article{derrien2019detection,
author={derrien, olivier},
journal={journal of the audio engineering society},
title={detection of genuine lossless audio files: application to the mpeg-aac codec},
year={2019},
volume={67},
number={3},
pages={116-123},
doi={https://doi.org/10.17743/jaes.2019.0002},
month={march},
abstract={in this study the author describes an algorithm that can discriminate transcoded from true lossless audio files, i.e. a file that was obtained by directly ripping a genuine audio cd from a file that was decoded from a lossy audio format (e.g. mp3, aac) and re-encoded with a lossless format. this algorithm should allow both consumers and online music dealers to check the authenticity of lossless music files. although research focuses on the mpeg aac codec, the approach can also be applied to the mp3 codec. it is based on the detection of quantization errors in the time-frequency domain without using machine learning. tests with a large database of 1576 audio files, original and transcoded with aac from itunes, show null false positive ratios and very low false negative ratios, possibly null for high-precision settings, which is better than other methods. it was shown that a perfectly accurate classification can be reached, provided a sufficiently long computation time is allowed. furthermore, the method is naturally immune to truncation of audio files and global gain modification.},}

TY - paper
TI - Detection of Genuine Lossless Audio Files: Application to the MPEG-AAC Codec
SP - 116 EP - 123
AU - Derrien, Olivier
PY - 2019
JO - Journal of the Audio Engineering Society
IS - 3
VO - 67
VL - 67
Y1 - March 2019 TY - paper
TI - Detection of Genuine Lossless Audio Files: Application to the MPEG-AAC Codec
SP - 116 EP - 123
AU - Derrien, Olivier
PY - 2019
JO - Journal of the Audio Engineering Society
IS - 3
VO - 67
VL - 67
Y1 - March 2019
AB - In this study the author describes an algorithm that can discriminate transcoded from true lossless audio files, i.e. a file that was obtained by directly ripping a genuine audio CD from a file that was decoded from a lossy audio format (e.g. MP3, AAC) and re-encoded with a lossless format. This algorithm should allow both consumers and online music dealers to check the authenticity of lossless music files. Although research focuses on the MPEG AAC codec, the approach can also be applied to the MP3 codec. It is based on the detection of quantization errors in the time-frequency domain without using machine learning. Tests with a large database of 1576 audio files, original and transcoded with AAC from iTunes, show null false positive ratios and very low false negative ratios, possibly null for high-precision settings, which is better than other methods. It was shown that a perfectly accurate classification can be reached, provided a sufficiently long computation time is allowed. Furthermore, the method is naturally immune to truncation of audio files and global gain modification.