Abstract
The evolution of modern cameras, mobile phones equipped with sophisticated image editing software has revolutionized digital imaging. In the process of image editing, contrast enhancement is a very common technique to hide visual traces of tampering. In our work, we have employed statistical distribution of block variance and AC DCT coefficients of an image to detect global contrast enhancement in an image. The variation in statistical parameters of block variance and AC DCT coefficients distribution for different degrees of contrast enhancement are used as features to detect contrast enhancement. An SVM classifier with 10-fold cross-validation is employed. An overall accuracy greater than 99% in detection with false rate less than 2% has been achieved. The proposed method is novel and it can be applied to uncompressed, previously JPEG compressed and post enhancement JPEG compressed images with high accuracy. The proposed method does not employ oft-repeated image histogram-based approach.
Highlights
The advent of cellular phones with high resolution and sophisticated cameras has ushered revolution in our lives
The variation in statistical parameters of block variance and AC Discrete Cosine Transform (DCT) coefficients distribution for different degrees of contrast enhancement are used as features to detect contrast enhancement
Digital devices are loaded with lots of media editing and enhancement softwares which enable common man to play with both image sources as well as information. This has led to the development of Digital Image Forensics (DIF), an area of research which targets certifying the veracity of images
Summary
The advent of cellular phones with high resolution and sophisticated cameras has ushered revolution in our lives. To create a visually imperceptible modification, it is required to match lighting conditions, re-size, rotate, or stretch portions of the images, re-save the final image (typically with lossy compression such as JPEG), etc These manipulations result in introduction of specific correlations in the statistics of images, which on detection can serve as a sign of digital tampering at detector. We have employed a two-parameter Gamma distribution from exponential family [13] of distributions to characterize the block variance of both unenhanced and contrast enhanced images. An SVM classifier [15] with Gaussian Radial Basis Function (RBF) is applied to classify between unenhanced and enhanced images For both JPEG compressed as well as uncompressed images, the detection accuracy of the proposed method is high. Statistical Modeling of Block Variance and AC DCT Coefficients where Kv(x) modified Bessel function of the third kind
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