NDLI: Improved detection of MP3 double compression using content-independent features

Content Provider	IEEE Xplore Digital Library
Author	Mengyu Qiao Sung, A.H. Qingzhong Liu
Copyright Year	2013
Description	Author affiliation: Dept. of Math. & Comput. Sci., South Dakota Sch. of Mines & Technol., Rapid City, SD, USA (Mengyu Qiao) \|\| Dept. of Comput. Sci., Sam Houston State Univ., Huntsville, TX, USA (Qingzhong Liu) \|\| Dept. of Comput. Sci. & Eng., Additive Syst. Anal. New Mexico Tech, Socorro, NM, USA (Sung, A.H.)
Abstract	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.
Starting Page	1
Ending Page	4
File Size	705296
Page Count	4
File Format	PDF
ISBN	9781479910274
DOI	10.1109/ICSPCC.2013.6664121
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Media Encoding SVM Digital forgery Support vector machines MP3 Quantization (signal) Accuracy Double compression Pattern classification Feature extraction DENFIS Audio Digital audio players
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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