EXPLOITING NOISE AND TEXTURAL FEATURES FOR PASSIVE IMAGE FORENSICS

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Published: Oct 20, 2017
DOI: https://doi.org/10.26483/ijarcs.v8i8.3397
Keywords:
Image tampering, noise discrepancies, feature extraction, textural information, quality metrics, statistical evaluation

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Surbhi Gupta
Research Scholar, I.K. Gujral Punjab Technical University, Jalandhar, India
Neeraj Mohan
Parvinder Singh Sandhu

Abstract

Advancement in information technology and image processing has resulted in image tampering and its forensics. This paper aims at detecting image manipulations by exploiting the various noise and texture characteristics of the image. Various filters and edge detectors are used to capture the noise and texture characterizes of the image. Statistical features such as mean and variance are extracted and then utilized for differentiating original image from manipulated one. SVM classifier is used for classifying images as authentic or forged. Experiments proved that proposed integrated technique performs much better than existing state of art techniques.

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Vol. 8 No. 8 (2017): September-October
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References

A. Rocha, W. Scheirer, T. Boult, S. Goldenstein, “Vision of the unseen: current trends and challenges in digital image and video forensicsâ€. ACM Computer Survey, 2011; 43(4):26:1–26:42.

H. Farid, “Detecting digital forgeries using bispectral analysisâ€, Technical Report AIM-1657, AI Lab, Massachusetts Institute of Technology; 1999.

G. K. Birajdar and V. H. Mankar, “Digital image forgery detection using passive techniques: A surveyâ€, Digital Investigation, 2013, 10(3), 226-245.

Li. Xin, “Blind image quality assessmentâ€, Proceedings of International Conference on Image Processing, 2002, Vol. 1. IEEE.

I. Avcibas, S. Bayram, N. Memon, M. Ramkumar, B. Sankur, “A classifier design for detecting image manipulationsâ€, Proc. International conference on image processing (ICIP), 2004. p. 2645–8.

A. C. Popescu and H. Farid, “Exposing digital forgeries by detecting duplicated image regionsâ€, Technical Report TR 2004-515, Department of Computer Science, Dartmouth College, 2004.

S. Bayram, I. Avcibas, B. Sankur and N. Memon, “Image manipulation detection â€, Electron Imaging, 2006;15(4). 041102-1–041102-17.

H. Gou, A. Swaminathan, M. Wu, “Noise features for image tampering detection and steganalysisâ€, Proc. International conference on image processing (ICIP) 2007. p. 97–100.

Z. Zhang, J. Kang, Y. Ren, “An effective algorithm of image splicing detectionâ€, Proc. International conference on computer science and software engineering, 2008. p. 1035–9.

B. Mahdian and S. Saic, “Using noise inconsistencies for blind image forensicsâ€. Image and Vision Computing, 2009, 27(10), 1497-1503.

W. Wang, J. Dong and T. Tan, “Effective image splicing detection based on image chromaâ€. 16th IEEE International Conference on Image Processing (ICIP), 2009, (pp. 1257-1260). IEEE.

F. Battisti, M. Carli and A. Neri, “Image forgery detection by means of no-reference quality metricsâ€. IS&T/SPIE Electronic Imaging, 2012, pp. 83030K-83030K, International Society for Optics and Photonics.

Y. Ke, Q. Zhang, W. Min and S. Zhang, “Detecting Image Forgery Based on Noise Estimationâ€, 2014, International Journal of Multimedia and Ubiquitous Engineering, 9(1), 325-336.

B. Liu and C. M. Pun, “Splicing Forgery Exposure in Digital Image by Detecting Noise Discrepanciesâ€, 2015, International Journal of Computer and Communication Engineering, 4(1), 33.

Y. Q. Shi, C. Chen and W. Chen, “A natural image model approach to splicing detectionâ€, Proc. of the 9th Workshop on Multimedia & Security (ACM), 51–62 (2007).

Z. He, W. Lu, W. Sun and J. Huang, “Digital image splicing detection based on Markov features in DCT and DWT domainâ€, Pattern Recognition, 45(12), 4292–4299 (2012).

G. Muhammad, M. S. Dewan, M. Moniruzzaman, M. Hussain and M. N. Huda, “Image forgery detection using Gabor filters and DCTâ€, Proc. IEEE Int. Conf. Electrical Engineering Inf. Communication Technol., Dhaka, Bangladesh, 1–5 (2014).

M. Hussain, S. Qasem, G. Bebis, G. Muhammad, H. Aboalsamh and H. Mathkour, “Evaluation of image forgery detection using multi-scale Weber local descriptorsâ€, International Journal on Artificial Intelligence Tools, 24(4), 1540016-1540042 (2015).

X. Shen, Z. Shi, and H. Chen, “Splicing image forgery detection using textural features based on the grey level co-occurrence matricesâ€, IET Image Processing, 11(1), 44-53 (2016).

J. Dong and W. Wang, “CASIA tampered image detection evaluation databaseâ€, http://forensics.idealtest.org (2011).

C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machinesâ€, ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, (2011).