Abstract
Image splicing is very common and fundamental in image tampering. Many splicing detection schemes based on Markov features in transform domain have been proposed. Based on previous studies, the traditional DWT based schemes perform not better than the DCT based schemes. In this paper, a block DWT based scheme is proposed to improve the detection performance of the DWT based scheme. Firstly, the block DWT is applied on the source image. Then, the Markov features are constructed in block DWT domain to characterize the dependency among wavelet coefficients across positions. After that, feature selection method SVM-RFE is used to reduce the dimensionality of features. Finally, Support Vector Machine is exploited to classify the authentic and spliced images. Experiment results show that the detection performance of the features extracted in DWT domain can be improved with block DWT based scheme. And then, in order to further clarify the phenomenon about the traditional DWT based schemes perform not better than the DCT based schemes, a detail comparison between the two kinds of schemes is proposed based on a set of experiments. The results show that the DWT based scheme is more applicable and powerful than the DCT based scheme, and the DCT based scheme is more suitable for handling these datasets which generated with the process of JPEG compression.
Similar content being viewed by others
References
Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226–245
Chang CC, Lin CJ (2010) LIBSVM – a library for support vector machines. http://www.csie.ntu.edu.tw/cjlin/libsvm
Chen L, Lu W, Ni J (2012) An image region description method based on step sector statistics and its application in image copy-rotate/flip-move forgery detection. Int J Digit Crime and Forensics 4(1):49–62
Chen L, Lu W, Ni J, Sun W, Huang J (2013) Region duplication detection based on harris corner points and step sector statistics. J Vis Commun Image Represent 24(3):244–254
Christopoulos C, Skodras A, Ebrahimi T (2000) The JPEG2000 still image coding system: An overview. IEEE Trans Consum Electron 46(4):1103–1127
Cozzolino D, Gragnaniello D, Verdoliva L (2014) Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques. In: IEEE international conference on image processing (ICIP), pp 5297–5301
Cozzolino D, Gragnaniello D, Verdoliva L (2014) Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques. In: IEEE international conference on image processing (ICIP), pp 5302–5306
Elwin JGR, Aditya TS, Shankar SM (2010) Survey on passive methods of image tampering detection. In: International conference on communication and computational intelligence (INCOCCI), vol 7, pp 431–436
Farid H, Lyu S (2003) Higher-order wavelet statistics and their application to digital forensics. In: Computer vision and pattern recognition workshop, pp 94–94
Farid H, Lyu S (2003) Higher-order wavelet statistics and their application to digital forensics. In: International conference on computer vision and pattern recognition workshop, pp 1–8. Madison, Wisconsin, USA
Guo Y, Ding G, Liu L, Han J, Shao L (2017) Learning to hash with optimized anchor embedding for scalable retrieval. IEEE Trans Image Process 26 (3):1344–1354
Guo Y, Ding G, Han J (2018) Robust quantization for general similarity search. IEEE Trans Image Process 27(2):949–963
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422
Gyamfi KS, Brusey J, Hunt A, Gaura E (2018) Linear dimensionality reduction for classification via a sequential bayes error minimisation with an application to flow meter diagnostics. Expert Syst Appl 91:252–262
He Z, Lu W, Sun W, Huang J (2012) Digital image splicing detection based on markov features in DCT and DWT domain. Pattern Recogn 45(12):4292–4299
Hsu CW, Chang CC, Lin CJ (2010) A practical guide to support vector classification. Department of Computer Science, National Taiwan University. http://www.csie.ntu.edu.tw/cjlin/papers/guide/guide.pdf
Kodovský J, Fridrich J, Holub V (2012) Ensemble classifiers for steganalysis of digital. IEEE Trans Inf Forensics Secur 7(2):432–444
Li C, Ma Q, Xiao L, Li M, Zhang A (2017) Image splicing detection based on markov features in qdct domain. Neurocomputing 228:29–36
Li J, Najmi A, Gray R (2000) Image classification by a two dimensional hidden markov model. In: IEEE transactions on signal processing, pp 517–533
Li J, Yang F, Lu W, Sun W (2016) Keypoint-based copy-move detection scheme by adopting mscrs and improved feature matching. Multimedia Tools and Applications, pp 1–15
Li K, He F, Yu H (2018) Robust visual tracking based on convolutional features with illumination and occlusion handing. J Comput Sci Technol 33(1):223–236
Li K, He F, Yu H, Chen X (2017) A correlative classifiers approach based on particle filter and sample set for tracking occluded target. Appl Math-A J Chin Univ 32(3):294–312
Li K, He F, YU H, Chen X (2017) A parallel and robust object tracking approach synthesizing adaptive bayesian learning and improved incremental subspace learning. Frontiers of Computer Science. https://doi.org/10.1007/s11704-018-6442-4
Lu W, Sun W, Chung FL, Lu H (2011) Revealing digital fakery using multiresolution decomposition and higher order statistics. Eng Appl Artif Intel 24 (4):666–672
Luo W, Qu Z, Pan F, Huang J (2007) A survey of passive technology for digital image forensics. Front Comput Sci Chin 1(2):166–179
Mahdian B, Saic S (2010) A bibliography on blind methods for identifying image forgery. Signal Process Image Commun 25:389–399
Ng TT, Chang SF (2004) A data set of authentic and spliced image blocks. Tech. Rep. 203-2004-3, Columbia University
Nie L, Wang M, Zha ZJ, Chua TS (2012) Oracle in image search: a content-based approach to performance prediction. ACM Trans Inf Syst 30(2):1–23
Panchal UH, Srivastava R (2015) A comprehensive survey on digital image watermarking techniques. In: International conference on communication systems and network technologies (CSNT), pp 591–595
Potdar VM, Han S, Chang E (2005) A survey of digital image watermarking techniques. In: 3rd IEEE international conference on industrial informatics (INDIN), pp 709–716. Perth, Western Australia
Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: The 9th ACM workshop on multimedia and security, pp 51–62. ACM, Dallas, Texas, USA
Srivastava A, Lee A, Simoncelli E, Zhu SC (2003) On advances in statistical modeling of natural images. J Math Imaging Vision 18(1):17–33
Sun J, He F, Chen Y, Chen X (2016) A multiple template approach for robust tracking of fast motion target. Appl Math-A J Chin Univ 31(2):177–197
Sutthiwan P, Shi YQ, Su W, Ng TT (2010) Rake transform and edge statistics for image forgery detection. In: IEEE international conference on multimedia and expo (ICME), pp 1463–1468
The dresden image database. http://forensics.inf.tu-dresden.de/ddimgdb/locations. [Online; accessed 6-July-2016]
Verdoliva L, Cozzolino D, Poggi G (2014) A feature-based approach for image tampering detection and localization. In: IEEE international workshop on information forensics and security (WIFS), pp 149–154
Vyas C, Lunagaria M (2014) A review on methods for image authentication and visual cryptography in digital image watermarking. In: IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–6
Wang M, Li H, Tao D, Lu K, Wu X (2012) Multimodal graph-based reranking for web image search. IEEE Trans Image Process 21(11):4649–4661
Wang M, Yang K, Hua XS, Zhang HJ (2010) Towards a relevant and diverse search of social images. IEEE Trans Multimed 12(8):829–842
Wang W, Dong J, Tan T (2010) Image tampering detection based on stationary distribution of markov chain. In: IEEE international conference on image processing (ICIP), pp 2101–2104
Yan X, He F, Chen Y (2017) A novel hardware/software partitioning method based on position disturbed particle swarm optimization with invasive weed optimization. J Comput Sci Technol 32(2):340–355
Yan X, He F, Hou N, Ai H (2018) An efficient particle swarm optimization for large-scale hardware/software co-design system. Int J Cooperative Inf Syst 27(1):1–28
Yang F, Li J, Lu W, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intel 59:73–83
Yu H, He F, Pan Y (2018) A novel region-based active contour model via local patch similarity measure for image segmentation. Multimedia Tools and Applications, pp 1–23
Zhang Z, Qiu G, Sun Q, Lin X (2004) A unified authentication framework for jpeg2000. In: IEEE international conference on multimedia and expo (ICME), pp 915–918
Zhang Q, Lu W, Weng J (2016) Joint image splicing detection in DCT and Contourlet transform domain. J Vis Commun Image Represent 40(12):449–458
Zhou Y, He F, Qiu Y (2017) Dynamic strategy based parallel ant colony optimization on gpus for tsps. SCIENCE CHINA Inf Sci 60(6):068,102:1C068,102:3
Zhou Y, He F, Hou N, Qiu Y (2018) Parallel ant colony optimization on multi-core simd cpus. Futur Gener Comput Syst 79:473–487
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. U1736118), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Key Project of Scientific Research Plan of Guangzhou (No. 201804020068), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, Q., Lu, W., Wang, R. et al. Digital image splicing detection based on Markov features in block DWT domain. Multimed Tools Appl 77, 31239–31260 (2018). https://doi.org/10.1007/s11042-018-6230-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-018-6230-z