Image splicing detection technique based on Illumination-Reflectance model and LBP

Abstract

A Copy-create digital image forgery is image tampering that merges two or more areas of images from different sources into one composite image; it is also known as image splicing. Excellent forgeries are so tricky that they are not noticeable to the naked eye and don’t reveal traces of tampering to traditional image tamper detection techniques. To tackle this image splicing detection problem, machines learning-based techniques are used to instantly discriminate between the authentic and forged image. Numerous image forgery detection methods to detect and localize spliced areas in the composite image have been proposed. However, the existing methods with high detection accuracy are computationally expensive since most of them are based on hybrid feature set or rely on the complex deep learning models, which are very expensive to train, run on expensive GPUs, and require a very large amount of data to perform better. In this paper, we propose a simple and computationally efficient image splicing forgery detection that considers a trade-off between performance and the cost to the users. Our method involves the following steps: first, luminance and chrominance are found from the input image; second, illumination is estimated from Luminance using Illumination-Reflectance model; third, Local Binary Patterns normalized histogram for illumination and Chrominance is computed and used as the feature vector for classification using the following machine learning algorithms: Support Vector Machine, Linear Discriminant Analysis, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes. Extensive experiments on the public dataset CASIA v2.0 show that the new algorithm is computationally efficient and effective for image splicing tampering detection.