FF-Net: An End-to-End Feature-Fusion Network for Double JPEG Detection and Localization

Abstract

In the real-world, most images are saved in JPEG format, so many forged images are partially or totally composed of JPEG images and then saved in JPEG format again. In this case, exposing forged images can be accomplished by the detection of double JPEG compressions. Although the detection methods of double JPEG compressions have greatly improved, they rely on handcrafted features of image patches and cannot locate forgery at pixel-level. To break this limitation, we propose an end-to-end feature-fusion network (FF-Net) for double compression detection and forgery localization. We find that JPEG compression fingerprint primarily exists on the high-frequency component of an image, and the singly and doubly compression yield different fingerprints. Therefore, we design two encoders cooperatively to learn the compression fingerprint directly from the whole image. A decoder is deployed to locate the regions with different compression fingerprints at pixel-level based on the learned compression fingerprint. The experiment results verify that the proposed FF-Net can detect and locate the forged regions more accurately than these existing detection methods. Besides, it has a good generalization ability that the network trained on one compression case can work in numerous compression cases. Moreover, it can detect different local forgeries, including copy-move, splicing, and object-removal.