D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization

TL;DR

This paper introduces D-Unet, a dual-encoder neural network that improves image splicing forgery detection and localization by combining learned image fingerprints with directional information, outperforming existing methods.

Contribution

The novel dual-encoder architecture with an unfixed and fixed encoder enhances detection accuracy without needing extensive pre-training or large forgery datasets.

Findings

D-Unet outperforms state-of-the-art methods in detection accuracy.

It is robust against various attack types.

No large-scale pre-training required.

Abstract

Recently, many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these detection methods focus on the local patches or local objects. In fact, image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. However, some specific image contents are hardly retained by CNN-based detection networks, but if included, would improve the detection accuracy of the networks. To resolve these issues, we propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network. This dual-encoder is followed by a spatial pyramid global-feature extraction module that expands the global insight of D-Unet for classifying the tampered and non-tampered regions more accurately. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection, without requiring pre-training or training on a large number of forgery images. Moreover, it was stably robust to different attacks.