A fuzzy fusion approach for modified contrast enhancement based image forensics against attacks

In today’s digital age the trustworthy towards image is distorting because of malicious forgery images. The issues related to the multimedia security have led to the research focus towards tampering detection. The main objective of the work is to develop robust and forensic detection framework against post processing. It is also essential to enhance the security against attacks. In this paper, a Modified Contrast Enhancement based Forensics (MCEF) method based on Fuzzy Fusion is proposed against post-processing activity. First, we check for the histogram peaks and gaps as a result of contrast enhancement which is used in the latest technique. From the standpoint of attackers, we use two types of attacks, CE trace hiding attack and CE trace forging attack, which could invalidate the forensic detector and fabricate two types of forensic errors, consequently. The CE trace hiding attack is implemented by integrating local random dithering into the form of pixel value mapping. The CE trace forging attack is proposed by modifying the grey level histogram of a target pixel region to fraudulent peak/gap artifacts. Then both attacks are added to enhanced images as a post processing activity. As a result the gaps get disappeared, but introduced sudden peaks. Then, feature selection methods in conjunction with fuzzy fusion approach is suggested to enhance the robustness of tamper detection methods. The threshold value for contrast detection is increased, so we can identify the contrast enhancement. The Artificial Neural Network (ANN) is used instead of SVM, it increases the robustness and accuracy of the digital images. The proposed methodology will be implemented using MATLAB and validated by comparing with the conventional techniques.

[1]  Alessandro Piva,et al.  Detection of Nonaligned Double JPEG Compression Based on Integer Periodicity Maps , 2012, IEEE Transactions on Information Forensics and Security.

[2]  Hany Farid,et al.  Exposing digital forgeries by detecting traces of resampling , 2005, IEEE Transactions on Signal Processing.

[3]  James F. O'Brien,et al.  Exposing photo manipulation with inconsistent reflections , 2012, TOGS.

[4]  K. Vijayalakshmi,et al.  Detecting contrast enhancement based image forgeries by parallel approach , 2015, 2015 2nd International Conference on Electronics and Communication Systems (ICECS).

[5]  Alex ChiChung Kot,et al.  Manipulation Detection on Image Patches Using FusionBoost , 2012, IEEE Transactions on Information Forensics and Security.

[6]  Antara Bhattacharya,et al.  Revealing image forgery through image manipulation detection , 2015, 2015 Global Conference on Communication Technologies (GCCT).

[7]  Yücel Altunbasak,et al.  A Histogram Modification Framework and Its Application for Image Contrast Enhancement , 2009, IEEE Transactions on Image Processing.

[8]  K. J. Ray Liu,et al.  Forensic detection of image manipulation using statistical intrinsic fingerprints , 2010, IEEE Transactions on Information Forensics and Security.

[9]  Alex ChiChung Kot,et al.  Estimating EXIF Parameters Based on Noise Features for Image Manipulation Detection , 2013, IEEE Transactions on Information Forensics and Security.

[10]  Mohamed Deriche,et al.  A bibliography of pixel-based blind image forgery detection techniques , 2015, Signal Process. Image Commun..

[11]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[12]  Mauro Barni,et al.  Second-Order Statistics Analysis to Cope With Contrast Enhancement Counter-Forensics , 2015, IEEE Signal Processing Letters.

[13]  H. Farid,et al.  Image forgery detection , 2009, IEEE Signal Processing Magazine.

[14]  Yao Zhao,et al.  Contrast Enhancement-Based Forensics in Digital Images , 2014, IEEE Transactions on Information Forensics and Security.

[15]  Sabu Emmanuel,et al.  ACE–An Effective Anti-forensic Contrast Enhancement Technique , 2016, IEEE Signal Processing Letters.

[16]  Derek Nowrouzezahrai,et al.  Learning hatching for pen-and-ink illustration of surfaces , 2012, TOGS.

[17]  Min Wu,et al.  Digital image forensics via intrinsic fingerprints , 2008, IEEE Transactions on Information Forensics and Security.

[18]  Nasir D. Memon,et al.  Image manipulation detection , 2006, J. Electronic Imaging.

[19]  Yao Zhao,et al.  Forensic estimation of gamma correction in digital images , 2010, 2010 IEEE International Conference on Image Processing.

[20]  Babak Mahdian,et al.  A bibliography on blind methods for identifying image forgery , 2010, Signal Process. Image Commun..

[21]  Hany Farid,et al.  Statistical Tools for Digital Forensics , 2004, Information Hiding.

[22]  Tomás Pevný,et al.  Detection of Double-Compression in JPEG Images for Applications in Steganography , 2008, IEEE Transactions on Information Forensics and Security.

[23]  Shih-Chia Huang,et al.  Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution , 2013, IEEE Transactions on Image Processing.

[24]  Jiwu Huang,et al.  Blind Detection of Median Filtering in Digital Images: A Difference Domain Based Approach , 2013, IEEE Transactions on Image Processing.