Tampering Detection using Resampling Features and Convolution Neural Networks

Abstract

The increased usage of image editing tools has resulted in the ease of manipulating multimedia data such as images. These manipulations affect the truthfulness and legitimacy of images, resulting in misinterpretation and may affect social stability. The image forensic technique has been utilized for detecting whether an image is tampered with using certain attacks such as splicing, copy-move, etc.This paper presents an efficient tampering detection method using resampling features (RSF) and Convolution neural network (CNN). In RSF-CNN, during preprocessing the image is divided into homogenous patches. Then, within each patch resampling features are extracted by exploiting affine transformation and Laplacian operator. Then, features extracted are aggregated for constructing descriptors using Convolution neural network. Extensive analysis is carried out for evaluating tampering detection and tampered region segmentationaccuracies of proposed RSF-CNN based tampering detection methodologies considering various distortions and post-processing attacks such as joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and multiple manipulations. From the result achieved it can be seen the RSF-CNN based tampering detection model achieves much better accuracies than existing tampering detection methodologies.