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Methods of Digital Image Forgery Detection

Methods of Digital Image Forgery Detection

Digital images and videos have become an essential part of our lives for a long time already. This fact made them the object of interest for forensic examiners and researchers. Here are the main problems that can be solved during a forensic examination:

  • searching for digital images or videos on digital media;
  • reconstruction of fragmented or damaged files;
  • improving quality of an image;
  • detection of means and circumstances of digital images or videos production;
  • digital images or videos forgery detection.

Nowadays there are a lot of methods and tools capable of detecting forgery. Apart from such common methods, as visual examination of an image, including parameters correction (such as brightness, contrast, etc), the following methods are used by forensic community:

  • shades and reflections;
  • lighting;
  • thumbnails analysis;
  • error level analysis, ELA;
  • luminance gradient analysis;
  • principal component analysis, PCA;
  • stamp, clone detection;
  • chromatic aberration analysis;
  • noise analysis.

Also there are such methods, as file structure analysis, metadata analysis and some others, which are more often used for detection means of images or videos production, but not for forgery detection. It’s clear that traces of an image editor in file’s metadata is quite suspicious, but changes in metadata or quantization index mismatch can’t be used as evidence of forgery (excluding evident repeating quantization).

Even without methods of detection means of images or videos production, provided list of methods is quite long. The thing is – different methods of images encoding and diversity of modifications, that can be used for digital images, change image’s properties available for forensic examination differently.

For example, ELA method can be used only for JPEG images, searching for cloned parts won’t help to find single insertion or manual retouch, shadows analysis won’t help with pictures captured in cloudy weather, etc.

So it’s very important for examiner to understand the reason of image forgery, methods available for detected type of alteration and methods of its detection.

Next time we will talk about using discussed methods of forgery detection in practice.


1. J. O’Brien and H. Farid. Exposing Photo Manipulation with Inconsistent Reflections. ACM Transactions on Graphics, 31(1):4:1-4:11, 2012.

Click to access tog12.pdf

2. E. Kee, J. O’Brien, and H. Farid. Exposing Photo Manipulation from Shading and Shadows. ACM Transactions on Graphics, 33(5):165:1-165:21, 2014.

Click to access tog14.pdf

3. M.K. Johnson and H. Farid. Exposing Digital Forgeries by Detecting Inconsistencies in Lighting. ACM Multimedia and Security Workshop, New York, NY, 2005.

Click to access acm05.pdf

4. M.K. Johnson and H. Farid. Exposing Digital Forgeries in Complex Lighting Environments. IEEE Transactions on Information Forensics and Security, 2(3):450-461, 2007.

Click to access tifs07a.pdf

5. Detecting Forged (Altered) Images.

6. A Picture’s Worth: Digital Image Analysis and Forensics. Dr. Neal Krawetz, Hacker Factor Solutions, August 2007. Presented at the Black Hat Briefings 2007.

Click to access bh-dc-08-krawetz-WP.pdf

7. A.C. Popescu and H. Farid. Exposing Digital Forgeries by Detecting Duplicated Image Regions. TR2004-515, Department of Computer Science, Dartmouth College, September 2004.

8. M.K. Johnson and H. Farid. Exposing Digital Forgeries Through Chromatic Aberration. ACM Multimedia and Security Workshop, Geneva, Switzerland, 2006.

9. Xunyu Pan, Xing Zhang, Siwei Lyu. Exposing Image Splicing with Inconsistent Local Noise Variances


About the authors:

Serge Petrov

Interests: Digital Video Forensics, Forgery Detection, Audio Forensics

Igor Mikhaylov

Interests: Computer, Cell Phone & Chip-Off Forensics

Oleg Skulkin

Interests: iOS forensics, Android forensics, Mac OS X forensics, Windows forensics, Linux forensics

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