Abstract
With the technology progress, a plethora of freely accessible software has questioned the authenticity of digital images. This field is continuously creating challenges for researchers to ascertain the integrity of images. Hence, there is a need to improve the performance of forgery detection algorithms from time to time. This paper is focused on the detection of splicing forgery because it is one of the most frequently used image manipulation techniques. In the proposed scheme, Markov features in both Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) domains are extracted and combined for the detection of image splicing. Three-level DWT is applied to the source image by the means of discrete Haar wavelet. The image is split in to high and low-frequency sub-bands after applying one level DWT. Furthermore, low-frequency sub-band is decomposed twice to obtain three-level DWT, which leads to more information and less amount of noise. The efficacy of the proposed scheme has been appraised on six benchmark datasets i.e. CASIA v2.0, DVMM, IFS-TC, CASIA v1.0, Columbia, and DSO-1. Moreover, the SVM classifier is trained to classify the images as tampered or authentic. The effectiveness of the proposed scheme is evaluated based on various performance parameters such as accuracy, sensitivity, specificity, and informedness. The proposed results show improved accuracy i.e. 99.69%, 99.76%, 97.80%, 98.61%, 96.90%, and 92.50% on CASIA v1.0, CASIA v2.0, DVMM, Columbia, IFS-TC, and DSO-1, respectively, in comparison to other existing approaches.
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References
Abdallah EE, Hamza AB, Bhattacharya P (2007) Spectral graph-theoretic approach to 3D mesh watermarking. In: Proceedings of graphics interface, pp 327–334
Abdallah EE, Hamza AB, Bhattacharya P (2007) Improved image watermarking scheme using fast Hadamard and discrete wavelet transforms. J Electron Imaging 16(3):033020
Abdallah EE, Hamza AB, Bhattacharya P (2009) Watermarking 3D models using spectral mesh compression. Signal Image Video Process 3(4):375–389
Abdallah EE, Otoom AF, Abdallah AE, Bsoul M, Awwad S (2019) A hybrid secure watermarking scheme using nonnegative matrix factorization and FastWalsh-Hadamard transform. J Appl Secur Res 1:1–14
Agarwal S, Chand S (2016) Texture operator based image splicing detection hybrid technique. In: proceedings of international conference on Computational Intelligence & Communication Technology (CICT), pp 116-120
Agarwal S, Chand S (2016) Image forgery detection using Markov features in undecimated wavelet transform. In: ninth international conference on contemporary computing (IC3), pp 1-6
Alahmadi AA, Hussain M, Aboalsamh H, Muhammad G, Bebis G (2013) Splicing image forgery detection based on DCT and local binary pattern. In: proceedings of global conference on signal and information processing (GlobalSIP), pp 253-256
Alahmadi A, Hussain M, Aboalsamh H, Muhammad G, Bebis G, Mathkour H (2017) Passive detection of image forgery using DCT and local binary pattern. Signal Image Video Process 11(1):81–88
Chen C, Shi YQ (2008) JPEG image steganalysis utilizing both intrablock and interblock correlations. In: IEEE international symposium on circuits and systems (ISCAS 2008), pp 3029-3032
Cozzolino D, Gragnaniello D, Verdoliva L (2014) Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques. In: proceedings of international conference on image processing (ICIP), pp 5302-5306
Crouse MS, Nowak RD, Baraniuk RG (1998) Wavelet-based statistical signal processing using hidden Markov models. IEEE Trans Signal Process 46(4):886–902
De Carvalho TJ, Riess C, Angelopoulou E, Pedrini H, de Rezende RA (2013) Exposing digital image forgeries by illumination color classification. IEEE Trans Inf Forensics Secur 8(7):1182–1194
Dong J, Wang W, Tan T, Shi YQ (2008) Run-length and edge statistics based approach for image splicing detection. In: International workshop on digital watermarking, pp 76–87
Dong J, Wang W, Tan T (2013) Casia image tampering detection evaluation database. In: proceedings of China Summit & International Conference on signal and information processing (ChinaSIP), pp 422-426
El-Alfy ES, Qureshi MA (2017) Robust content authentication of gray and color images using lbp-dct markov-based features. Multimed Tools Appl 76(12):14535–14556
Emam M, Qi H, Xiamu N (2016) PCET based copy-move forgery detection in images under geometric transforms. Multimed Tools Appl 75(18):11513–11527
Fan J, Chen T, Kot AC (2017) EXIF-white balance recognition for image forensic analysis. Multidim Syst Sign Process 28(3):795–815
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 YF, Chang SF (2006) Detecting image splicing using geometry invariants and camera characteristics consistency. In: Proceedings of International Conference on Multimedia and Expo, pp 549–552
Hussain M, Saleh SQ, Aboalsamh H, Muhammad G, Bebis G (2014) Comparison between WLD and LBP descriptors for non-intrusive image forgery detection. In: proceedings of international symposium on innovations in intelligent systems and applications (INISTA), pp 197-204
Jalab HA, Subramaniam T, Ibrahim RW, Kahtan H, Noor NF (2019) New texture descriptor based on modified fractional entropy for digital image splicing forgery detection. Entropy 21(4):371–379
Jeyasudha A, Priya K (2016) Object recognition based on LBP and discrete wavelet transform. Int J Adv Signal Image Sci 2(1):24–30
Kanwal N, Girdhar A, Kaur L, Bhullar JS (2020) Digital image splicing detection technique using optimal threshold based local ternary pattern. Multimed Tools Appl 23(1):1–8
Kaur M, Gupta S (2016) A passive blind approach for image splicing detection based on DWT and LBP histograms. In: International symposium on security in computing and communication, Singapore, Springer, pp 318–327
Kirchner M, Fridrich J (2010) On detection of median filtering in digital images. Proc SPIE Electron Imaging Media Forensic Secur II 7541:1–12
Kumar V, Gupta P (2012) Importance of statistical measures in digital image processing. Int J Emerg Technol Adv Eng 2(8):56–62
Kumar A, Prakash CS, Maheshkar S, Maheshkar V (2019) Markov feature extraction using enhanced threshold method for image splicing forgery detection. In: Smart Innovations in Communication and Computational Sciences, pp 17–27
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
Mayer O, Stamm MC (2018) Accurate and efficient image forgery detection using lateral chromatic aberration. IEEE Trans Inf Forensics Secur 13(7):1762–1777
Muhammad G, Al-Hammadi MH, Hussain M, Bebis G (2014) Image forgery detection using steerable pyramid transform and local binary pattern. Mach Vis Appl 25(4):985–995
Muqeet MA, Holambe RS (2019) Local binary patterns based on directional wavelet transform for expression and pose-invariant face recognition. Appl Comput Inf 15(2):163–171
Ng TT, Chang SF, Sun Q (2004) A data set of authentic and spliced image blocks. Columbia University, ADVENT Technical Report, pp 203–2004
Powers DM (2011) Evaluation: from precision recall and f-measure to roc informedness markedness and correlation. J Mach Learn Technol 2(1):37–63
Prakash CS, Kumar A, Maheshkar S, Maheshkar V (2018) An integrated method of copy-move and splicing for image forgery detection. Multimed Tools Appl 77(20):26939–26963
Roy A, Bhalang Tariang D, Subhra Chakraborty R, Naskar R (2018) Discrete cosine transform residual feature based filtering forgery and splicing detection in JPEG images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 1552–1560
Sheng H, Shen X, Lyu Y, Shi Z, Ma S (2018) Image splicing detection based on Markov features in discrete octonion cosine transform domain. IET Image Process 12(10):1815–1823
Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: proceedings of the 9th workshop on multimedia & security, pp 51-62
Srivastava P, Khare A (2017) Integration of wavelet transform, local binary patterns and moments for content-based image retrieval. J Vis Commun Image Represent 42(1):78–103
Su B, Yuan Q, Wang S, Zhao C, Li S (2014) Enhanced state selection Markov model for image splicing detection. EURASIP J Wirel Commun Netw 2014(7):1–10
Sun XW, Li YJ, Chen Y (2008) Application of local standard deviation filtering in image processing [J]. Electron Opt Control 15(9):32–34
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
Vaishnavi D, Subashini TS (2016) Recognizing image splicing forgeries using histogram features. In: proceedings of international conference on big data and Smart City (ICBDSC), pp 1-4
Yu H, He F, Pan Y et al (2016) An efficient similarity-based level set model for medical image segmentation. J Adv Mech Syst, Manuf 10(8):JAMDSM0100-JAMDSM0100
Yu H, He F, Pan Y (2018) A novel region-based active contour model via local patch similarity measure for image segmentation. Multimed Tools Appl 77(18):24097–24119
Yu H, He F, Pan Y (2019) A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. Multimed Tools Appl 78(9):11779–11798
Yu H, He F, Pan Y (2020) A scalable region-based level set method using adaptive bilateral filter for noisy image segmentation. Multimed Tools Appl 79(9):5743–5765
Zhang Y, Zhao C, Pi Y, Li S (2012) Revealing image splicing forgery using local binary patterns of DCT coefficients. In: Communications, Signal Processing, and Systems, pp 181–189
Zhang Q, Lu W, Weng J (2016) Joint image splicing detection in DCT and Contourlet transform domain. J Vis Commun Image Represent 40:449–458
Zhang H, Wang C, Zhou X (2017) Fragile watermarking based on LBP for blind tamper detection in images. J Inf Process Syst 13(2):385–399
Zhang Z, Wang C, Zhou X (2018) A survey on passive image copy-move forgery detection. J Inf Process Syst 14(1):6–31
Zhang Q, Lu W, Wang R, Li G (2018) Digital image splicing detection based on Markov features in block DWT domain. Multimed Tools Appl 77(23):31239–31260
Zhao X, Wang S, Li S, Li J (2015) Passive image-splicing detection by a 2-d noncausal markov model. IEEE Trans Circuits Syst Video Technol 25(2):185–199
Hakimi F, Hariri M, GharehBaghi F (2015) Image splicing forgery detection using local binary pattern and discrete wavelet transform. In 2nd international conference on knowledge-based engineering and innovation. Tehran, IEEE, pp 1074–1077
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Kaur, N., Jindal, N. & Singh, K. A passive approach for the detection of splicing forgery in digital images. Multimed Tools Appl 79, 32037–32063 (2020). https://doi.org/10.1007/s11042-020-09275-w
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DOI: https://doi.org/10.1007/s11042-020-09275-w