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Digital Image Forensics

Mohamed AkhilSupervisor: Dr. Jimmy Li

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Project aim:

• To develop technique in automatic identification of fake digital photos.

• Implement environment for simulation and application of technique.

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Digital Image Forensics: is a method to investigate if the image is tampered or not.

Which photo is authentic (A or B)?

A BImages from <http://bpastudio.csudh.edu/FAC/LPRESS/471/readings/fakephotoskerrybush.htm>

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Other example of fake image:

Images from <http://bpastudio.csudh.edu/FAC/LPRESS/471/readings/fakephotoskerrybush.htm>

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Existing problems :

• Many programs are readily available such Adobe Photoshop to make fake photos.

• It is very difficult to recognize some altered images visually.

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Detection Techniques:• A technique to distinguish digitally altered images from

authentic images.

• Different scenarios to produce different type of fake image need different techniques to detect.

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1. Re-sampled Images:

• To create a convincing match, it is often necessary to re-size, rotate, or stretch the original images (or portions of them).

• Introduces specific correlations between neighbouring image pixels.

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2. Detection of Duplicated Image Regions:

• Tampered image with copying and pasting portions of the image to conceal a person or object in the scene.

Image from “Statistical Tools for Digital Image Forensics”, Popescu, A. 2004.

A B C

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3. Blind Estimation of Background Noise:

• Two different images from two different cameras will have different signal to noise ratio. If these two images are splicing together, the signal to noise ratio can be an aspect to investigate.

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4. Manipulated Colour Filter Array (CFA) Interpolated Images:

• Most the digital cameras are provided by single CCD or CMOS sensor which records single colour sample and the other two colour samples have to be interpolated from the neighbouring samples . Most modern digital cameras acquire images using a single image sensor overlaid with a Colour Filter Array (CFA).

• The most common configuration used to generate CFA is Bayer filter.

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• A digital image process used to reconstruct a full colour image from the incomplete colour samples output from an image sensor overlaid with a colour filter array (CFA) is called a demosaicking algorithm.

• the principal key of the CFA interpolation produces a periodic correlations.

• Possible Detection method if the CFA interpolation correlations are missing in any portion of an image or if any part of the image is repeated.

CFA Demosaicking:

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One possible Method:

• Expectation-Maximization Algorithm can be used for missing periodic correlations.

AImage from “Statistical Tools for Digital Image Forensics”, Popescu, A. 2004.

B

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Applications:

• The media, newspapers, and magazines. The media, newspapers, and magazines are legally responsible for what they publish. • Private use.

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Proposed Schedule:Month Action

August and September (2013) Project Planning and Literature Review

October , November, and December (2013)

Implementation and technique’s development

January, and February (2014) Technique Testing – real world applications

March and April (2014) Thesis, Result Presentation

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Conclusion:

• Improve the chosen technique to identify the tampered images.

• Simulate different scenarios of fake images for testing.

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Literature review References:• Farid, H. 2008. A Survey of Image Forgery Detection, Viewed 9 Sep 2013,

<http://www.cs.albany.edu/~lsw/homepage/DIF-S10_files/spm09.pdf>.• Popescu, A. 2004, Statistical Tools for Digital Image Forensics , viewed 20 Aug 2013,

<http://www.cs.dartmouth.edu/farid/downloads/publications/ih04.pdf>.• Popescu, A., Farid, H, 2005, Exposing Digital Forgeries in Color Filter Array Interpolated

Images, viewed 10 Sep 2013, <http://www.ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1511009&tag=1>.

• John Kerr and Jane Fonda, viewed 17 Sep 2013, <http://www.bpastudio.csudh.edu/FAC/LPRESS/471/readings/fakephotoskerrybush.htm>

• Ghatol, N., Paigude, R. , shirke, A. 2013, Image Morphing Detection by Locating

Tampered Pixels with Demosaicing Algorithms , International Journal of Computer Applications , viewed 20 Sep 2013.

• Borman, S. 2009, The Expectation Maximization Algorithm A short tutorial, 9 Jan, viewed 23 Sep 2013.