Two improved forensic methods of detecting contrast enhancement in digital images

Abstract

Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement:Citation: Xufeng Lin ; Xingjie Wei and Chang-Tsun Li "Two improved forensic methods of detecting contrast enhancement in digital images", Proc. SPIE 9028, Media Watermarking, Security, and Forensics 2014, 90280X (February 19, 2014) Copyright notice: © (2014) Copyright Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. DOI abstract link: http://dx.doi.org/10.1117/12.2038644 A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. For more information, please contact the WRAP ABSTRACTContrast enhancements, such as histogram equalization or gamma correction, are widely used by malicious attackers to conceal the cut-and-paste trails in doctored images. Therefore, detecting the traces left by contrast enhancements can be an effective way of exposing cut-and-paste image forgery. In this work, two improved forensic methods of detecting contrast enhancement in digital images are put forward. More specifically, the first method uses a quadratic weighting function rather than a simple cut-off frequency to measure the histogram distortion introduced by contrast enhancements, meanwhile the averaged high-frequency energy measure of histogram is replaced by the ratio taken up by the high-frequency components in the histogram spectrum. While the second improvement is achieved by applying a linear-threshold strategy to get around the sensitivity of threshold selection. Compared with their original counterparts, these two methods both achieve better performance in terms of ROC curves and real-world cut-and-paste image forgeries. The effectiveness and improvement of the two proposed algorithms are experimentally validated on natural color images captured by commercial camera.

Contrast Enhancement Estimation for Digital Image Forensics

“…In this work, we describe a new method to estimate contrast enhancement from images, taking advantage of the nature of contrast enhancement as a mapping between integral pixel values and the distinct characteristics it introduces to the pixel histogramof the 7 We did not use the grid-search based Algorithm 4 to handle the parametric contrast enhancement as in practical scenarios we usually do not have the knowledge of the types of contrast enhancement involved. 8 These images can be downloaded from https://mig.nist.gov/MediforBP/MediforBPResources.html.…”

“…Optimizing . We use the equivalent definition of 1 distance based on the cumulative distributions (7), and the problem of solving reduces to min ∑︀…”

“…A straightforward approach would be to apply the global contrast enhancement detection method to non-overlapping rectangular blocks of pixels [4,7,8]. However, this simple method has several problems.…”

“…There have been several methods to estimate contrast enhancement from an image [3][4][5][6][7][8][9]. However, these methods have two main limitations.…”

Analysis and Comparison of Methods for Improving Contrast of Images

“…Аналіз останніх досліджень та публікацій. Зусиллями досліджень минулого десятиліття було запропоновано ряд схем [1][2][3][4][5][6][7][8][9] для виявлення зображень зі збільшеним контрастом за рахунок використання атрибутів контрастного підсилення. Стамм та інші [1,2,3] виявили, що контрастне посилення вводить піки і прогалини в гістограму зображення, що призводить до появи в гістограмі зображень специфічних значень.…”