A Discriminative Statistical Model for Digital Image Forgery Detection

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Amira Baumy 1,* Naglaa. F Soiliman 2 Mahmoud Abdalla 3 Fathi Abd El-Samie 4

1. Obour Institute of Engineering and Technology, Obour, Egypt.

2. Faculty of Engineering, Zagazig University, Zagazig, Egypt.

3. Faculty of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

4. Faculty of Electronic Engineering, Menoufia University,Menouf, Egypt.

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2016.06.01

Received: 20 Jul. 2016 / Revised: 1 Sep. 2016 / Accepted: 29 Sep. 2016 / Published: 8 Nov. 2016

Index Terms

Forgery detection, Homomorphic filter, image histogram


The headway of modern technology and facility to use processing software leads to tamper and implicate of digital images. This tampering is being performed without leaving any a clear effect noted with the naked eye. The discrimination between different authentic and forged images can be based on its Probability Density Functions (PDFs). This paper introduces a new model for digital image forgery detection. This framework has two main phases; training and testing. In the training phase, the peak is calculated for the derivatives histogram of the illumination components by using homomorphic filter to separate the illumination components on each image. Firstly, the derivative of illumination histogram for authentic and forged images is calculated then the PDFs are estimated for authentic and forged images, finally the threshold is determined. In the testing phase, the determined threshold is tested with realistic dataset followed by using the selected bins for feature calculation in the prediction process. In the final prediction step, a detection and decision process is performed to obtain performance of the new model. This new model is provided a very effective performance. Different color image contrast systems RGB and HIS are studied and utilized for testing our model and compare between each channel for two systems to estimate performance and obtain more sensitive channel.

Cite This Paper

Amira Baumy, Naglaa. F Soiliman, Mahmoud Abdalla, Fathi Abd El-Samie,"A Discriminative Statistical Model for Digital Image Forgery Detection", International Journal of Engineering and Manufacturing(IJEM), Vol.6, No.6, pp.1-14, 2016. DOI: 10.5815/ijem.2016.06.01


[1]S. Mushtaq and A. H. Mir, "Digital Image Forgeries and Passive Image Authentication Techniques: A Survey ", International Journal of Advanced Science and Technology vol.73, pp.15-32, 2014.

[2]M. D. Ansari, S. P. Ghreraa and V. Tragi, "Pixel-Based Image Forgery Detection: A Review", IETE Journal of Education, 55:1, pp.40-46, 2014.

[3]D. Sharma, and P. Abrol, " Digital Image Tampering – A Threat to Security Management", International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 10, October, 2013.

[4]T. Qazi, K. Hayat1, S. U. Khan, and S. A. Madani, I. A. Khan1, "Survey on Blind Image Forgery Detection", IET Image Process, vol. 7, Iss. 7, pp. 660–670, 2013.

[5]G. K. Birajdara, and V. H. Mankar," Digital Image Forgery Detection using Passive Techniques: A survey", Digital Investigation 10, pp. 226–245, 2013.

[6]H. Farid, "A Survey of Image Forgery Detection", IEEE Signal Proc Mag., vol. 2, no. 26, pp. 6–25, 2009. 

[7]M. Sengupta and J. K. Mandal, "Authentication through Hough Transformation Generated Signature on G-Let D3 Domain (AHSG)", International Conference on Computational Intelligence: Modeling Techniques and Applications, 2013.

[8]X. Wang, J. Xue, Z. Zheng, Z. Liu and N. Li, "Image Forensic Signature for Content Authenticity Analysis", Vis. Commination. Image R., vol. 23, 2012.

[9]G. S. Spagnolo and M. DeSantis, "Holographic watermarking for authentication of cut images", Optics and Lasers in Engineering, vol. 49, pp. 1447–1455, 2011.

[10]Z. Moghaddasi, H. A. Jalab, R. Md Noor, "SVD-based Image Splicing Detection", International Conference on Information Technology and Multimedia (ICIMU) proceedings at IEEE, 2014.

[11]J. Hou, H. Shi, Y. Cheng, and Ran Li , "Forgery Image Splicing Detection by Abnormal Prediction Features", International Conference of mechatronic and automation proceedings at IEEE, 2013.

[12]P. S. Yun Q.; S. W. Su; Tian-Tsong Ng, "Rake Transform and Edge Statistics For Image Forgery Detection", IEEE Publication Year: 2010.

[13]G. Muhammad, "Multi-Scale Local Texture Descriptor For Image Forgery Detection", IEEE Publication , 2013.

[14]G. K. Birajdar, "Blind Authentication of Resampled Images and Rescaling Factor Estimation" IEEE Publication , 2013.

[15]Z. Kaizhen, and Z. Zhang,"A Novel Algorithm of Image Splicing Detection" International Conference on Industrial Control and Electronics Engineering, IEEE Publication Year: 2012.

[16]S.Murali, B. Chittapur, "comparison and analysis of photo image forgery detection techniques" International Journal on Computational Sciences & Applications (IJCSA), I Publication Year: 2012.

[17]J. Grim, and P Somol, "Digital Image Forgery Detection by Local Statistical Models" IEEE Publication Year: 2010.

[18]U. A. Nnolim, "Implementation of Spatial Domain Homomorphic Filtering on Embedded Mobile Devices", Nigerian journal of technology, 2015. 

[19]R. Jirık, "High-Resolution Ultrasonic Imaging Using Fast Two-Dimensional Homomorphic Filtering", IEEE Publication Year: 2006.

[20]D. MD, and A. S. Murthy, "A Comparison between Different Colour Image Contrast Enhancement Algorithms", IEEE Publication Year: 2011.

[21]T. B. Adji, "Negative Content Filtering Based on Skin Texture Homomorphic Filter and Localizations", International Conference of Electricial Engineering and Computer Science, IEEE Publication Year: 2014.

[22]Wei wang, "Effective Image Splicing Detection Based on Image Chrome", IEEE Publication Year: 2009.

[23]G. LIANG, and D. CHANG , "Colour Image Enhancement with Exact HIS Colour Model", International journal of innovative computing 2011.