Abstract
To prove authenticity and originality of images, many techniques were recently released. This paper proposes a pixel based forgery detection technique for identifying forged images, which is an effective method for finding tampering in images. The proposed method detects splicing and copy-move forgery in images by locating the forged components in the input image. Splicing is the process of copying a component from an image and pasted to another image. Copy-move forgery is the process of copying a component from an image and pasted to another portion of the same image. To find the forged component in the input image, the noise variance remaining after denoising method and SURF features are used. In order to locate spliced component, image segmentation is done before finding the number of components in the image. For segmentation, segmentation based on combining spectral and texture features are used. To identify the number of components in the image, fuzzy c-means clustering is used. In order to locate copy-move forgery, SURF features are detected first and extracted for finding the similarity between keypoints. The experiment results show that the proposed method is very good at identifying whether an image is forged or not. The proposed method gives a high speed performance compared to the state-of-the-art methods. Results gained through the experiments on both manually edited images and visually realistic real images shows the effectiveness of the proposed method.
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References
Lee, J.-C., Chang, C.-P., Chen, W.-K.: Detection of copy–move image forgery using histogram of orientated gradients. Inf. Sciences (2015)
Amerini, I., Ballan, L., Caldelli, R., et al.: Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Processing: Image Communication 28, 659–669 (2013)
Chang, I.-C., Yu, J.C., Chang, C.-C.: A forgery detection algorithm for exemplar-based inpainting images using multi-region relation. Image and Vision Computing 31, 57–71 (2013)
Bravo-Solorio, S., Nandi, A.K.: Automated detection and localisation of duplicated regions affected by reflection, rotation and scaling in image forensics. Signal Process. 91, 1759–1770 (2011)
Pan, X., Lyu, S.: Region duplication detection using image feature matching. IEEE Trans. on Inf. Forensics and Secur. 5(4), 857–867 (2010)
Zhang, W., Cao, X., Qu, Y., et al.: Detecting and extracting the photo composites using planar homography and graph cut. IEEE Trans. Inf. Forensics Secur. 5(3), 544–555 (2010)
He, Z., Lu, W., et al.: Digital image splicing detection based on markov features in dct and dwt domain. Pattern Recognit. 45(2012), 4292–4299 (2012)
Birajdar, G.K., Mankar, V.H.: Digital image forgery detection using passive tech-niques: a survey. Digit. Investig. 10, 226–245 (2013)
Liu, K.C.: Color image watermarking for tamper proofing and pattern-based recovery. IET Image Process. 6(5), 445–454 (2012)
Han, Q., Han, L., et al.: Dual watermarking for image tamper detection and self-recovery. In: Proc. of the 9th Int. Conference on Intell. Inf. Hiding and Multimedia Signal Process., pp. 33–36 (2013)
Lukas, J., Fridrich, J., Goljan, M.: Digital camera identification from sensor pattern noise. IEEE Trans. on Inf. Forensics and Secur. 1(2), 205–214 (2006)
Li, C.-T.: Source camera identification using enhanced sensor pattern noise. IEEE Trans. on Inf. Forensics and Secur. 5(2), 280–287 (2010)
Kang, X., Li, Y., Qu, Z., Huang, J.: Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans. on Inf. Forensics and Secur. 7(2), 393–402 (2012)
Tomioka, Y., Ito, Y., Kitazawa, H.: Robust digital camera identification based on pairwise magnitude relations of clustered sensor pattern noise. Trans. on Inf. Forensics and Secur. 8(12), 1986–1995 (2013)
Wu-Chih, H., Dai, J.-S., Jian, J.-S.: Effective composite image detection method based on feature inconsistency of image components. Digit. Signal Processing 39, 50–62 (2015)
Mahdian, B., Saic, S.: Using noise inconsistencies for blind image forensics. Image Vis. Compu., Special Section: Computer Vision Methods for Ambient Intell. 10, 1497–1503 (2009)
Comaniciu, D., Meer, P.: Mean shift:a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)
Levin, A., Rav-Acha, A., Lischinski, D.: Spectral matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1699–1712 (2008)
Hardie, R.C., Boncelet, C.G.: Lum filters:a class of rank-order-based filters for smoothing and sharpening. IEEE Trans. Signal Process. 41(3), 1061–1076 (1993)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. on Pattern Analysis and Machine Intell. 11(7), 674–693 (1989)
Yuan, J., Wang, D., Li, R.: Remote sensing image segmentation by combining spectral and texture Features. IEEE Trans. on Geoscience and Remote Sensing 52(1), 16–24 (2014)
Fan, J., Cao, H., Kot, A.C.: Estimating EXIF parameters based on noise features for image manipulation detection. IEEE Trans. Inf. Forensics Secur. 8(4), 608–618 (2013)
Bianchi, T., Piva, A.: Image forgery localization via block-grained analysis of JPEG artifacts. IEEE Trans. Inf. Forensics Secur. 7(3), 1003–1017 (2012)
Lin, Z., He, J., Tang, X., Tang, C.K.: Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognit. 42(11), 2492–2501 (2009)
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Williams, D., Krishnalal, G., Jagathy Raj, V.P. (2016). Fraudulent Image Recognition Using Stable Inherent Feature. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_2
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DOI: https://doi.org/10.1007/978-3-319-28658-7_2
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