Forensic AMR Double Compression Detection Using Linear Prediction Coefficients and Robust Scaling

摘要

The AMR (adaptive multirate) codec is a widespread standard of audio format used to store speech in smartphones or digital recorders. Thus, AMR double compression detection is an interesting topic for multimedia forensics. In this paper a new method is proposed to detect AMR double compression using compressed-domain features based on linear prediction coefficients and a support vector machine (SVM). By using a robust scaling procedure, a detection accuracy of 98% was achieved with TIMIT database, reaching the same performance as the state-of-the-art methods.