收稿日期: 2018-02-06
网络出版日期: 2018-03-31
基金资助
广东省科技计划国际协同创新项目基金（No.2017A050501002）；中新国际研究院基金（No.206-A017023）；广州市科技计划项目基金（No.201510010275）；广东省教育厅2015年重点平台及科研项目特色创新类项目基金（No.2015KTSCX100）资助
Blind Detection of Audio Forgery Based on ENF Neighborhood Correlation Coefcient
Received date: 2018-02-06
Online published: 2018-03-31
为了提高现有的基于电网频率（electric network frequency，ENF）信号的盲篡改检测方法在低信噪比时的检测准确率，提出一种利用ENF信号邻域相关系数的音频篡改盲检测方法.首先将待测音频中提取的ENF信号划分子块，并计算相邻区域子块的相关系数；然后对相关系数序列进行自适应快速横向滤波（fast transversal flter，FTF），根据误差能量的变化检测音频篡改.为了减小干扰的影响以提高篡改检测准确率和篡改定位精度，从正、反两个方向对音频进行上述处理，再结合两个方向的误差能量进行篡改检测.与现有的两种代表性方法相比，所提出的方法不但能精确定位篡改，而且能有效提高音频篡改的检测准确率，尤其是在ENF波动较大和信噪比较低的情况下更有优势.
关键词： 音频篡改盲检测; 快速横向滤波自适应算法; 误差能量; 电网频率信号; 双向处理
吕志胜, 谭丽, 封斌, 胡永健 . 基于ENF邻域相关系数的音频篡改盲检测[J]. 应用科学学报, 2018 , 36(2) : 287 -298 . DOI: 10.3969/j.issn.0255-8297.2018.02.008
In order to improve the accuracy of the existing blind tamper detection methods based on electric network frequency (ENF) in the case of low SNR, we propose a novel blind detection approach based on the ENF cross-correlation coefcients in neighborhood. First, the ENF signal extracted from the query audio is divided into blocks, and the cross-correlation coefcients of the adjacent blocks are calculated. Then the adaptive fast transversal flter (FTF) is performed to the coefcient sequence. According to the variation of the fltering error energy, we can detect audio forgery. In order to reduce the interference and improve the accuracy of forgery detection and localization, the audio is processed in both forward and backward directions. Then the two directions'error energies are combined to detect forgery. Compared with two existing representative methods, the proposed method performs excellent accuracy both in forgery location and forgery detection. Especially under the circumstances of larger ENF fluctuation and lower SNR, the method shows more advantages.
[1] Hajj-Ahmad A, Garg R, Wu M. ENF-based region-of-recording identifcation for media signals[J]. IEEE Transactions on Information Forensics and Security, 2015, 10(6):1125-1136.
[2] Hua G, Zhang Y, Goh J, Thing V L L. Audio authentication by exploring the absolute-errormap of ENF signals[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(5):1003-1016.
[3] Grigoras C. Applications of ENF criterion in forensic audio, video, computer and telecommunication analysis[J]. Forensic Science International, 2007, 167(2):136-145.
[4] Hajj-Ahmad A, Garg R, Wu M. ENF based location classifcation of sensor recordings[C]//International Workshop on Information Forensics and Security. IEEE, 2013:138-143.
[5] Garg R, Varna A L, Wu M. Seeing ENF:natural time stamp for digital video via optical sensing and signal processing[C]//International Conference on Multimedia. Scottsdale.ACM, 2011:23-32.
[6] Garg R, Varna A L, Hajj-Ahmad A, Wu M. "Seeing" ENF:power-signature-based timestamp for digital multimedia via optical sensing and signal processing[J]. IEEE Transactions on Information Forensics and Security, 2013, 8(9):1417-1432.
[7] Hua G, Goh J, Thing V L L. A dynamic matching algorithm for audio timestamp identifcation using the ENF criterion[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(7):1045-1055.
[8] Nicolalde-Rodriguez D P, Apolinario J A. Evaluating digital audio authenticity with spectral distances and ENF phase change[C]//International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009:1417-1420.
[9] Nicolalde-Rodriguez D P, Apolinario J A, Biscainho L W P. Audio authenticity:detecting ENF discontinuity with high precision phase analysis[J]. IEEE Transactions on Information Forensics and Security, 2010, 5(3):534-543.
[10] Hu Y J, Li C T, Lyu Z S, Liu B B. Audio forgery detection based on max offsets for cross correlation between ENF and reference signal[C]//International Workshop on Digital-Forensics and Watermarking.[s.l.]:Springer,2013:253-266.
[11] 吕志胜,胡永健,李晗,刘琲贝. 一种双重判断机制的音频篡改盲检测算法[J]. 华南理工大学学报(自然科学版), 2014, 42(8):70-76. Lü Z S, Hu Y J, Li H, Liu B B. A blind audio forgery detection algorithm based on dual judgment mechanisms[J]. Journal of South China University of Technology (Natural Science Edition), 2014, 42(8):70-76.(in Chinese)
[12] 皇甫堪,陈建文,楼生强. 现代数字信号处理[M]. 北京:电子工业出版社.
/
| 〈 |
|
〉 |
