Computer Science ›› 2020, Vol. 47 ›› Issue (6): 310-315.doi: 10.11896/jsjkx.190600081

• Information Security • Previous Articles     Next Articles

Image Forgery Detection Based on DCT Coefficients Hashing

SHANG Jin-yue, BI Xiu-li, XIAO Bin, LI Wei-sheng   

  1. Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Received:2019-06-14 Online:2020-06-15 Published:2020-06-10
  • About author:SHANG Jin-yue,born in 1993,postgra-duate.His main research interests include image processing and pattern re-cognition.
    BI Xiu-li,born in 1982,Ph.D,associate professor, is a member of China Computer Federation.Her main research interests include digital image processing and multimedia information security.

Abstract: With the continuous improvement of digital image processing technology,tampered images are flooded with the Internet and various media,seriously affecting people’s daily life.Therefore,digital image forensics technology,which can judge the authenticity and integrity of images,is particularly important.An image forgery detection algorithm based on DCT coefficients hashing was proposed,for dealing with the splicing forgery detection of digital images.In the process of JPEG compression,first,the DCT coefficient matrix of the Y channel after DCT is extracted,then the image hashing is constructed by DCT coefficients,and finally the image hashing is embedded in the header of file of the compressed code stream.At the time of tampering detection,a tampering image hashing is constructed by compressed code stream corresponding to the tampering image,and then compared with the embedded original image hashing for initial detection.In order to achieve the pixel-level detection,a method of secondary detection was proposed based on the preliminary detection results.The experimental results show that the proposed algorithm not only has good robustness,but also has a shorter hash length and a 10% higher detection accuracy.

Key words: 2D-DCT, Image forgery detection, Image hashing, JPEG compression

CLC Number: 

  • TP301
[1]MISHRA M.Digital Image Tamper Detection Techniques-AComprehensive Study[J].Computer Science,2013,2(1):1-12.
[2]VENKATESAN R,KOON S M,JAKUBOWSKI M H,et al.Robust Image Hashing[C]//International Conference on Image Processing.IEEE,2000.
[3]MONGA V,VATS D,EVANS B L.Image Authentication Under Geometric Attacks Via Structure Matching[C]//IEEE International Conference on Multimedia & Expo.IEEE,2005.
[4]ROY S,SUN Q.Robust Hash for Detecting and Localizing Image Tampering[C]//IEEE International Conference on Image Processing.IEEE,2007.
[5]AHMED F,SIYAL M Y,ABBAS V U.A secure and robust hash-based scheme for image authentication[J].Signal Proces-sing,2010,90(5):1456-1470.
[6]LU W,WU M.Multimedia forensic hash based on visual words[C]//IEEE International Conference on Image Processing.IEEE,2010.
[7]BATTIATO S,FARINELLA G M,MESSINA E,et al.Robust image alignment for tampering detection[J].IEEE Transactions on Information Forensics and Security,2012,7(4):1105-1117.
[8]LV X,WANG Z J.Perceptual Image Hashing Based on Shape Contexts and Local Feature Points[J].IEEE Transactions on Information Forensics and Security,2012,7(3):1081-1093.
[9]ZHAO Y,WANG S,ZHANG X,et al.Robust Hashing for Image Authentication Using Zernike Moments and Local Features[J].IEEE Transactions on Information Forensics and Security,2013,8(1):55-63.
[10]WANG X,PANG K,ZHOU X,et al.A Visual Model-Based Perceptual Image Hash for Content Authentication[J].IEEE Transactions on Information Forensics and Security,2015,10(7):1336-1349.
[11]YAN C P,PUN C M,YUAN X C.Multi-scale image hashing using adaptive local feature extraction for robust tampering detection[J].Signal Processing,2016,121(C):1-16.
[12]TANG Z,ZHANG X,LI X,et al.Robust Image Hashing with Ring Partition and Invariant Vector Distance[J].IEEE Transactions on Information Forensics and Security,2017,11(1):200-214.
[13]PUN C M,YAN C P,YUAN X C.Image Alignment based Multi-Region Matching for Object-level Tampering Detection[J].IEEE Transactions on Information Forensics and Security,2017,12(2):377-391.
[14]YAN C P,PUN C M,YUAN X C.Quaternion-based Image Hashing for Adaptive Tampering Localization[J].IEEE Tran-sactions on Information Forensics and Security,2016,11(12):2664-2677.
[15]YAN C P,PUN C M.Multi-Scale Difference Map Fusion for Tamper Localization using Binary Ranking Hashing[J].IEEE Transactions on Information Forensics & Security,2017,PP(99):2144-2158.
[16]LIU C,LING H,ZOU F,et al.Nonnegative sparse locality preserving hashing[J].Information Sciences,2014,281:714-725.
[17]ZHANG W,LIU Y,DAS S K,et al.Secure data aggregation in wireless sensor networks:A watermark based authentication supportive approach[J].Pervasive and Mobile Computing,2008,4(5):658-680.
[18]ELL T A,SANGWINE S J.Hypercomplex Fourier Transforms of Color Images[J].IEEE Transactions on Image Processing,2007,16(1):22-35.
[19]BATTIATO S,FARINELLA G M,MESSINA E,et al.Understanding geometric manipulations of images through bovw-based hashing[C]//2011 IEEE International Conference on Multimedia and Expo(ICME 2011).IEEE Computer Society,2011.
[20]GUO C,MA Q,ZHANG L.Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform[C]//2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 2008).Anchorage,Alaska,USA.IEEE,2008.
[1] LIU Ye, PAN Yan, XIA Rong-kai, LIU Di and YIN Jian. FP-CNNH:A Fast Image Hashing Algorithm Based on Deep Convolutional Neural Network [J]. Computer Science, 2016, 43(9): 39-46.
[2] Peng QiuMing;Yang XiaoFan;Huang Song;Li SaiJing;Bai Sen. [J]. Computer Science, 2005, 32(8): 63-66.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!