基于比例因子转移概率的AAC音频压缩历史检测算法

doi:10.11959/j.issn.1000-0801.2017277

Abstract

Abstract:

Audio compression history detection is an important part of audio forensics,which is important to detect whether audio has been tampered or forged.An algorithm of AAC audio compression history detection was presented by using the transition probability differences of scale factors as the discriminative feature.Experimental results demonstrate that the proposed method can distinguish the single,double and triple compressed AAC audios effectively,and from the low-bite-rate to high-bit-rate,the average classification accuracy achieves 99.75%,the same-bit-rate detection accuracy achieves 97.28%.In addition,the results of comparison experiments show that the proposed algorithm outperforms the state-of-the-art algorithm.

Key words: AAC audio, compression history, scale factor, transfer probability

CLC Number:

TP391

Qijuan HUANG,Rangding WANG,Diqun YAN. AAC compression detection based on scaling factor transition probability[J]. Telecommunications Science, 2017, 33(11): 93-101.

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References 15

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BR1	BR2=BR3
BR1	60	75	90	105	120	135	150
60	100%	100%	100%	100%	100%	100%	100%
75		94.5%	100%	100%	100%	100%	100%
90			97.5%	99.5%	100%	100%	100%
105				96.5%	100%	100%	100%
120					95.83%	100%	99.93%
135						98%	99.17%
150							96.67%

BR1	BR2=BR3
BR1	60	75	90	105	120	135	150
60	100%	100%	99.67%	100%	100%	100%	99.67%
75		96%	100%	99.83%	100%	99.83%	100%
90			96%	98.5%	99.83%	100%	100%
105				94.17%	100%	100%	100%
120					96.33%	99.83%	96.83%
135						95.5%	99%
150							95.17%

BR1	BR2=BR3
BR1	60	75	90	105	120	135	150
60	100%	99.83%	100%	100%	100%	100%	99.5%
75		96.83%	100%	100%	99.67%	99.83%	100%
90			98.67%	99.5%	100%	100%	97%
105				97.17%	100%	99.67%	100%
120					96.67%	99.83%	98.67%
135						96.67%	100%
150							97.67%

BR1	BR2=BR3
BR1	60	75	90	105	120	135	150
60	+22.83%	0	0	0	0	0	0
75		+15.92%	0	0	0	0	0
90			+21.75%	?0.33%	0	0	0
105				+22.87%	+1.5%	0	0
120					+16.66%	+2.33%	+0.03%
135						+15.92%	?0.26%
150							+11.17%

AAC compression detection based on scaling factor transition probability

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