Volume 5, Issue 1, March 2018
Hacking Intrinsic Fingerprints in Fractal Image Compression Using Genetic Algorithm
Over the decade, the world has completely depended upon digital images for communicating visual information. Many compression techniques have been used and compared to minimal the size of the image that has been transferred over the Internet. In this paper, we use the Fractal Image Compression which minimal the size of the image by using the property of self-similar or self-affine transformation and also has the feature of resolution independent. On the other side many forensic technique have been developed to verify the authenticity of digital images. One amongst and the most successful technique is to make use of an image's compression history and its associated compression fingerprints. But, there is a chance for anti-forensic techniques which are capable of fooling forensic algorithm. In this paper, we compress the image by using Genetic Algorithm in Fractal Image Compression. Then we develop a set of anti- forensic technique which is designed to remove significant indicators of compression from an image. For that, the first step is to develop a generalised framework for an anti-forensic technique for removing the compression fingerprints from an image transform coefficients. The framework which we developed operates by calculating the overall distribution of an image's transform coefficients before compression, after then adding anti- forensic dither to the transform coefficients of a compressed image such that their distribution matches the estimated one. This framework is then used to develop anti-forensic techniques for erasing compression fingerprints left by Fractal Image Compression. Through a series of experiments, we demonstrate that the anti- forensic technique which we developed is capable of removing forensically detectable traces of image compression without affecting an image's visual quality.
Anti-forensics, anti-forensic dither, framework ,transform coefficients, Fractal Image compression using Genetic Algorithm
Jamuna S R 1
M. Praveen Kumar 2
- Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.
- Aarha Technologies
Manuscript received : 04 December 2017
Manuscript revised : 12 January 2018
Accepted : 24 February 2018
Cite this article as:
Print ISSN : 2347 - 9280
Online ISSN : 2347 - 9299
Publisher Name : Sri Eshwar Publications, Coimbatore, Tamilnadu, India.