Tamper detection of social media images using quality artifacts and texture features

Maliciously edited images, also referred to as tampered or forged images, have become ubiquitous and challenging in recent times with the advent of social media platforms. Image splicing is a kind of image tampering where regions of images are copied and pasted into another image. Image tampering detection is a method for the authenticity verification of images produced as evidence in the court of law and forms a sub problem in image forensics. The aim of our work is to detect and locate the boundaries of image splicing, without a-priori knowledge of the reference image. We propose two methods, of which the first uses classification of texture patterns along with the standard deviations of block discrete cosine transformation of textures. In the second method, we combine the first with image quality artifacts incurred due to image tampering along with the entropy of histograms to get an integrated method. We use a support vector machine classifier for training and testing these approaches. Our methods are tested on two publicly available tamper detection datasets and the results are compared with seven other existing works. It is observed that our integrated method gives best results. We have also tested the proposed methods on a social media dataset and the results are encouraging.