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Improved detection of MP3 double compression using content-independent features

With the booming of smartphone and high-speed wireless networks in recent years, audio streaming and sharing become convenient and inexpensive, so that digital media is gradually replacing physical media. This trend has also led to more attacks to digital audio and its application. MP3 double compression, achieved by decompressing and recompressing audio to a different compression ratio, is a typical manipulation of audio for malicious purposes. In this paper, we propose an approach for detecting both up-transcoded and down-transcoded MP3 audio files and revealing the real compression quality based on statistical patterns extracted from quantized MDCT coefficients and their derivatives. To minimize the false prediction caused by individual characteristics of diversified audio clips, we generated reference audio signals by recompressing and calibrating the audio, and measured the differences between signal-based and reference-based features. Support vector machines and dynamic evolving neural-fuzzy inference systems were applied for binary and multi-class classifications. The experimental results show that our approach effectively detects MP3 double compression and exposes the audio processing history for digital forensics.