A Multi-Stream Fusion Network for Image Splicing Localization
This addresses the problem of detecting tampered images for forensic applications, representing an incremental improvement over existing methods.
The paper tackles image splicing localization by proposing a multi-stream fusion network that processes RGB images and handcrafted forensic signals in parallel, achieving state-of-the-art results such as 0.898 AUC on the CASIA dataset.
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.