Detection of AAC compression using MDCT-based features and supervised learning

Audio files are frequent targets of malicious users who seek illegal profit trading with fake-quality content. For increasing the confidence in the integrity of audio files, the detection of fake-quality content is an important task. This paper proposes a method for detecting Advanced Audio Coding (AAC) compression on suspicious WAV files, in which the variance of the Modified Discrete Cosine Transform (MDCT) characterises four compression bitrates: uncompressed, 64 kbps, 128 kbps, and 256 kbps. This scheme takes advantage of the reduction of the variance of the high-frequency MDCT coefficients in compressed signals. Data obtained from MDCT coefficients generate a high-dimensional feature space. Hence, Principal Component Analysis, followed by Linear Discriminant Analysis, is used for projecting the high-dimensional data onto a lower-dimensional space. Besides, six supervised learning algorithms are compared for classifying four compression bitrates. The experiments show that using audio samples with 20 seconds and 1024 MDCT coefficients, an accuracy of 93% is reached with a Bayesian classifier. Collaterally, the detection between uncompressed and compressed signals attains an accuracy of 97% with Multinomial Logistic Regression. In conclusion, the proposed approach can detect previous AAC compression and can be potentially used when it is unfeasible to recover the suspicious signal completely.