A Multi-Stream Fusion Network for Image Splicing Localization

TL;DR

This paper introduces a multi-stream encoder-decoder network that processes raw images and handcrafted signals separately for improved image splicing localization, achieving state-of-the-art results on public datasets.

Contribution

The paper proposes a novel multi-stream fusion network architecture that independently processes raw images and handcrafted forensic signals for enhanced splicing localization.

Findings

Achieves 0.898 AUC on CASIA dataset.

Outperforms several competing methods.

Demonstrates the effectiveness of multi-stream fusion.

Abstract

In this paper, we address the problem of image splicing localization with a multi-stream network architecture that processes the raw RGB image in parallel with other handcrafted forensic signals. Unlike previous methods that either use only the RGB images or stack several signals in a channel-wise manner, we propose an encoder-decoder architecture that consists of multiple encoder streams. Each stream is fed with either the tampered image or handcrafted signals and processes them separately to capture relevant information from each one independently. Finally, the extracted features from the multiple streams are fused in the bottleneck of the architecture and propagated to the decoder network that generates the output localization map. We experiment with two handcrafted algorithms, i.e., DCT and Splicebuster. Our proposed approach is benchmarked on three public forensics datasets, demonstrating competitive performance against several competing methods and achieving state-of-the-art results, e.g., 0.898 AUC on CASIA.