A new approach for JPEG resize and image splicing detection

Abstract

Today's ubiquitous digital media are easily tampered by, e.g., removing or adding objects from or into images without leaving any obvious clues. JPEG is a most widely used standard in digital images and it can be easily doctored. It is therefore necessary to have reliable methods to detect forgery in JPEG images for applications in law enforcement, forensics, etc. In this paper, based on the correlation of neighboring Discrete Cosine Transform (DCT) coefficients, we propose a method to detect resized JPEG images and spliced images, which are widely used in image forgery. In detail, the neighboring joint density features of the DCT coefficients are extracted; then Support Vector Machines (SVM) are applied to the features for detection. To improve the evaluation of JPEG resized detection, we utilize the shape parameter of generalized Gaussian distribution (GGD) of DCT coefficients to measure the image complexity.