融合多特征的视频帧间篡改检测算法

肖辉; 翁彬; 黄添强; 普菡; 黄则辉

doi:10.11959/j.issn.2096-109x.2020007

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融合多特征的视频帧间篡改检测算法

学术论文 | 更新时间：2024-06-03

- 融合多特征的视频帧间篡改检测算法
- Video inter-frame tampering detection algorithm fusing multiple features
- 网络与信息安全学报 2020年6卷第1期页码：84-93
- 作者机构：
  
  1. 福建师范大学数学与信息学院，福建福州 350007
  2. 福建省大数据挖掘与应用工程技术研究中心，福建福州 350007
  3. 世新大学，台湾台北 350108
- 作者简介：
  
  [ "肖辉（1991- ），男，福建建瓯人，福建师范大学硕士生，主要研究方向为信息安全、数字多媒体取证" ]
  [ "翁彬（1981- ），男，福建福州人，博士，福建师范大学讲师，主要研究方向为机器学习及应用" ]
  [ "黄添强（1971- ），男，福建仙游人，博士，福建师范大学教授、博士生导师，主要研究方向为机器学习、数字多媒体取证" ]
  [ "普菡（1995- ），女，河南平舆人，福建师范大学硕士生，主要研究方向为信息安全、数字多媒体取证" ]
  [ "黄则晖（1999- ），女，福建仙游人，主要研究方向为多媒体编辑与传播" ]
- 基金信息：
  
  国家重点研发计划专项基金资助项目;National Key Program for Developing Basic Science(2018YFC1505805);应用数学福建省高校重点实验室基金资助项目;Applied Mathematics Fujian Provincial Key Laboratory Project(SX201803)
- DOI：10.11959/j.issn.2096-109x.2020007
  中图分类号： TP393
- 网络首发：2020-02，
  
  纸质出版：2020-02-15
- 稿件说明：
移动端阅览
肖辉, 翁彬, 黄添强, 等. 融合多特征的视频帧间篡改检测算法[J]. 网络与信息安全学报, 2020,6(1):84-93.

Hui XIAO, Bin WENG, Tianqiang HUANG, et al. Video inter-frame tampering detection algorithm fusing multiple features[J]. Chinese Journal of Network and Information Security, 2020, 6(1): 84-93.
肖辉, 翁彬, 黄添强, 等. 融合多特征的视频帧间篡改检测算法[J]. 网络与信息安全学报, 2020,6(1):84-93. DOI： 10.11959/j.issn.2096-109x.2020007.

Hui XIAO, Bin WENG, Tianqiang HUANG, et al. Video inter-frame tampering detection algorithm fusing multiple features[J]. Chinese Journal of Network and Information Security, 2020, 6(1): 84-93. DOI： 10.11959/j.issn.2096-109x.2020007.

摘要

传统的视频帧间被动取证往往依赖单一特征，而这些特征各自适用于某类视频，对其他视频的检测精度较低。针对这种情况，提出一种融合多特征的视频帧间篡改检测算法。该算法首先计算视频的空间信息和时间信息值并对视频进行分组，接着计算视频帧间连续性VQA特征，然后结合SVM–RFE特征递归消除算法对不同特征排序，最后利用顺序前向选择算法和Adaboost二元分类器对排序好的特征进行筛选与融合。实验结果表明，该算法提高了篡改检测精度。

Abstract

Traditional passive forensics of video inter-frame tampering often relies on single feature.Each of these features is usually suitable for certain types of videos

while has low detection accuracy for other videos.To combine the advantages of these features

a video inter-frame tampering detection algorithm that could fuse multi-features was proposed.The algorithm firstly classified the input video into one group based on its space information and time information values.Then it calculated the VQA features that represented the video inter-frame continuity.These features were sorted by the SVM-RFE feature recursive elimination algorithm.Finally

the sorted features were filtered and fused by the sequential forward selection algorithm and Adaboost binary classifier.Experimental results show that the proposed algorithm could achieve higher tampering detection accuracy.

关键词

Keywords

references

INDEX C V N . Forecast and methodology,2016–2021 [R ] . White Paper,2017 , 6 .

IULIANI M , SHULLANI D , FONTANI M , et al . A video forensic framework for the unsupervised analysis of MP4-like file container [J ] . IEEE Transactions on Information Forensics and Security , 2018 , 14 ( 3 ): 635 - 645 .

AFCHAR D , NOZICK V , YAMAGISHI J , et al . Mesonet:a compact facial video forgery detection network [C ] // 2018 IEEE International Workshop on Information Forensics and Security (WIFS) . 2018 : 1 - 7 .

MATERN F , RIESS C , STAMMINGER M . Exploiting visual artifacts to expose deepfakes and face manipulations [C ] // 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) . 2019 : 83 - 92 .

SINGH R D , AGGARWAL N . Video content authentication techniques:a comprehensive survey [J ] . Multimedia Systems , 2018 , 24 ( 2 ): 211 - 240 .

CHAO J , JIANG X , SUN T . A novel video inter-frame forgery model detection scheme based on optical flow consistency [C ] // International Workshop on Digital Watermarking . 2012 : 267 - 281 .

WU Y , JIANG X , SUN T , et al . Exposing video inter-frame forgery based on velocity field consistency [C ] // 2014 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP) . 2014 : 2674 - 2678 .

LIU Y , HUANG T . Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis [J ] . Multimedia Systems , 2017 , 23 ( 2 ): 223 - 238 .

WANG W , FARID H . Exposing digital forgeries in video by detecting double MPEG compression [C ] // The 8th Workshop on Multimedia and Security . 2006 : 37 - 47 .

AGHAMALEKI J A , BEHRAD A . Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding [J ] . Signal Processing:Image Communication , 2016 , 47 : 289 - 302 .

LI F , HUANG T . Video copy-move forgery detection and localization based on structural similarity [C ] // The 3rd International Conference on Multimedia Technology (ICMT 2013) . 2014 : 63 - 76 .

张珍珍 , 侯建军 , 李赵红 , 等 . 基于 MSSIM 商一致性的视频插帧和删帧篡改检测 [J ] . 北京邮电大学学报 , 2015 , 38 ( 4 ): 84 - 88 .

ZHANG Z Z , HOU J J , LI Z H , et al . Video-farm insertion and deflection detection based on consistency of quotients of MSSIM [J ] . Journal of Beijing University of Posts and Telecommunications , 2015 , 38 ( 4 ): 84 - 88 .

WANG Z , BOVIK A C , SHEIKH H R , et al . Image quality assessment:from error visibility to structural similarity [J ] . IEEE Transactions on Image Processing , 2004 , 13 ( 4 ): 600 - 612 .

WANG Z , SIMONCELLI E P , BOVIK A C . Multiscale structural similarity for image quality assessment [C ] // The Thrity-Seventh Asilomar Conference on Signals,Systems ＆ Computers,2003 . 2003 , 2 : 1398 - 1402 .

CHANDLER D M , HEMAMI S S . VSNR:a wavelet-based visual signal-to-noise ratio for natural images [J ] . IEEE Transactions on Image Processing , 2007 , 16 ( 9 ): 2284 - 2298 .

SHEIKH H R , BOVIK A C . Image information and visual quality [J ] . IEEE Transactions on Image Processing , 2006 , 15 ( 2 ): 430 - 444 .

WANG Z , BOVIK A C . A universal image quality index [J ] . IEEE Signal Processing Letters , 2002 , 9 ( 3 ): 81 - 84 .

SHEIKH H R , BOVIK A C , DE VECIANA G . An information fidelity criterion for image quality assessment using natural scene statistics [J ] . IEEE Transactions on Image Processing , 2005 , 14 ( 12 ): 2117 - 2128 .

DAMERA-VENKATA N , KITE T D , GEISLER W S , et al . Image quality assessment based on a degradation model [J ] . IEEE Transactions on Image Processing , 2000 , 9 ( 4 ): 636 - 650 .

MANNOS J , SAKRISON D . The effects of a visual fidelity criterion of the encoding of images [J ] . IEEE Transactions on Information Theory , 1974 , 20 ( 4 ): 525 - 536 .

ZHANG L , SHEN Y , LI H . VSI:a visual saliency-induced index for perceptual image quality assessment [J ] . IEEE Transactions on Image Processing , 2014 , 23 ( 10 ): 4270 - 4281 .

ZHANG L , ZHANG L , MOU X , et al . FSIM:a feature similarity index for image quality assessment [J ] . IEEE Transactions on Image Processing , 2011 , 20 ( 8 ): 2378 - 2386 .

WANG Z , LI Q . Information content weighting for perceptual image quality assessment [J ] . IEEE Transactions on Image Processing , 2011 , 20 ( 5 ): 1185 - 1198 .

LIU A , LIN W , NARWARIA M . Image quality assessment based on gradient similarity [J ] . IEEE Transactions on Image Processing , 2012 , 21 ( 4 ): 1500 - 1512 .

LARSON E C , CHANDLER D M . Most apparent distortion:full-reference image quality assessment and the role of strategy [J ] . Journal of Electronic Imaging , 2010 , 19 ( 1 ):011006.

ZHANG L , LI H . SR-SIM:a fast and high performance IQA index based on spectral residual [C ] // 2012 19th IEEE International Conference on Image Processing . 2012 : 1473 - 1476 .

ZHANG L , ZHANG L , MOU X . RFSIM:a feature based image quality assessment metric using Riesz transforms [C ] // 2010 IEEE International Conference on Image Processing . 2010 : 321 - 324 .

LIU T J , LIN W , KUO C C J . Image quality assessment using multi-method fusion [J ] . IEEE Transactions on Image Processing , 2012 , 22 ( 5 ): 1793 - 1807 .

LIN J Y , LIU T J , WU E C H , et al . A fusion-based video quality assessment (FVQA) index [C ] // Signal and Information Processing Association Annual Summit and Conference (APSIPA),2014 Asia-Pacific . 2014 : 1 - 5 .

HUANG T , ZHANG X , HUANG W , et al . A multi-channel approach through fusion of audio for detecting video inter-frame forgery [J ] . Computers ＆ Security , 2018 , 77 : 412 - 426 .

SHANABLEH T . Detection of frame deletion for digital video forensics [J ] . Digital Investigation , 2013 , 10 ( 4 ): 350 - 360 .

LIU T J , LIN W , KUO C C J . Image quality assessment using multi-method fusion [J ] . IEEE Transactions on Image Processing , 2012 , 22 ( 5 ): 1793 - 1807 .

INSTALLATIONS T , LINE L . Subjective video quality assessment methods for multimedia applications [J ] . Networks , 1999 , 910 ( 37 ):5.

GUYON I , WESTON J , BARNHILL S , et al . Gene selection for cancer classification using support vector machines [J ] . Machine Learning , 2002 , 46 ( 1-3 ): 389 - 422 .

HU Y J , SALMAN A H , WANG Y F , et al . Construction and evaluation of video forgery detection database [J ] . Journal of South China University of Technology (Natural Science) , 2017 , 45 ( 12 ): 57 - 64 .

QADIR G , YAHAYA S , HO A T . Surrey university library for forensic analysis (SULFA) of video content [C ] // IET Conference on Image Processing . 2012 : 1 - 6 .

PEDREGOSA F , VAROQUAUX G , GRAMFORT A , et al . Scikit-learn:machine learning in Python [J ] . Journal of Machine Learning Research , 2011 , 12 : 2825 - 2830 .

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