Abstract
The vulnerability of digital images is growing towards manipulation. This motivated an area of research to deal with digital image forgeries. The certifying origin and content of digital images is an open problem in the multimedia world. One of the ways to find the truth of images is finding the presence of any type of contrast enhancement. In this work, novel and simple machine learning tool is proposed to detect the presence of histogram equalization using statistical parameters of DC Discrete Cosine Transform (DCT) coefficients. The statistical parameters of the Gaussian Mixture Model (GMM) fitted to DC DCT coefficients are used as features for classifying original and histogram equalized images. An SVM classifier has been developed to classify original and histogram equalized image which can detect histogram equalized image with accuracy greater than 95% when false rate is less than 5%.
Highlights
The vulnerability of digital images is growing towards manipulation
All All All are passed through Global Histogram Equalization (GHE) and Adaptive Histogram Equalization (AHE) operation, and for preparation of feature matrix for each classification, Tab. 8: Accuracy (TPR, FPR) of proposed method and existing methods in HE based image Forensics
In case of GHE and AHE, detection accuracy is more than 99 % with false alarm less than 1 %, which is comparable to existing methods
Summary
A GMM is a pdf which is the weighted sum of densities of Gaussian components defined as. I i=1 i where x is D-dimensional continuous-valued data vector, wi for i = 1, 2, 3, ..., K are the weights of mixture and K is the number of components. The parameters of GMM are: mean as vector, covariance matrix, and mixture weights. These parameters can be collected to form a feature set (θ) b. A consistency in values of JS and KS concludes GMM as a good model of fit across original and different types of HE images
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