1. Digital Image Forensics
Mohamed Akhil
Supervisor: Dr. Jimmy Li
Good afternoon; my name is Mohamed, I am doing master of Electronic engineering, today I will present my proposal of 18 unit, the title of my thesis is a Digital Image Forensics. It will be supervised by Dr. Jimmy Li.

2. Project aim:
To develop technique in automatic identification of fake digital photos.
Implement environment for simulation and application of technique.
The aim of the project is to develop technique to identify tampered digital images, and to simulate the technique for different scenarios.

3. Digital Image Forensics: is a method to investigate if the image is tampered or not. Which photo is authentic (A or B)?
Digital image forensics is a method to identify if the image is an authentic or not.
How many of you think that the image A is a tempered image?
Actually, an image A is an Authentic image, where Bush holds the book upside down, but the photographer temper the image , so the position of the book modified to be in the correct position. As it appears in the image B. A B
Images from <

4. Other example of fake image:
This fake image attacked John Kerry in the election on 2004, in this fake image, there are two images splicing together to create the tempered image.
Images from <

5. Existing problems :
Many programs are readily available such Adobe Photoshop to make fake photos.
It is very difficult to recognize some altered images visually.
There are many programs available to make fake images, for instance, Adobe Photoshop, the performance of these programs are improved in editing images. So some fake images become difficult to be recognise visually.

6. 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.
There are many techniques used to identify different scenarios of forgery.

7. 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.
If there are two different images splicing together , they may have different size and rotation ,so, they should be modified to generate a fake image.

8. 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.
There are two portions in this image have very strong correlation. A B C
Image from “Statistical Tools for Digital Image Forensics”, Popescu, A

9. 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.
Each image has its signal to noise ratio which is different with other images, so When two images are splicing together to produce a fake image, the signal to noise ratio can be used to determine if the image is authentic or fake.

10. 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.
Nowadays, most the digital cameras are equipped by a single charge coupled device [CDD] or complementary metal oxide semiconductor [CMOS] sensor overlaid with Colour Filter Array (CFA) to record single samples while the other two samples will be interpolated from the neighbouring samples.
The most common filter used to generate CFA is Bayer filter.

11. CFA Demosaicking:
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 is a process to reconstruct the single samples of CFA to a full colour images. CFA Demosaicking produces a periodic correlation. Any missing of the specified correlation means that the image is altered.

12. One possible Method:
Expectation-Maximization Algorithm can be used for missing periodic correlations. A B
One of the possible techniques can be used to determine missing periodic correlations is Expectation-Maximization Algorithm.
In this image, these portions of the image are maintained to be covered, in this case the periodic correlation is destroyed so the forgery can be determine by using Manipulated Colour Filter Array (CFA).
Image from “Statistical Tools for Digital Image Forensics”, Popescu, A

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

14. 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

15. Conclusion:
Improve the chosen technique to identify the tampered images.
Simulate different scenarios of fake images for testing.
This project is to imp

16. Literature review References:
Farid, H A Survey of Image Forgery Detection, Viewed 9 Sep 2013, <
Popescu, A. 2004, Statistical Tools for Digital Image Forensics , viewed 20 Aug 2013, <
Popescu, A., Farid, H, 2005, Exposing Digital Forgeries in Color Filter Array Interpolated Images, viewed 10 Sep 2013, <
John Kerr and Jane Fonda, viewed 17 Sep 2013, <
Ghatol, N., Paigude, R. , shirke, A , 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.