论文标题
用于图像拼接本地化的多流融合网络
A Multi-Stream Fusion Network for Image Splicing Localization
论文作者
论文摘要
在本文中，我们通过多流网络体系结构解决了图像拼接本地化的问题，该网络体系结构与其他手工制作的法医信号并行处理原始RGB图像。与以前仅使用RGB图像或以频道方式堆叠多个信号的方法不同，我们提出了一个由多个编码器流组成的编码器编码器体系结构。每个流都用篡改的图像或手工信号馈送，并分别处理它们以独立捕获每个信息的信息。最后，从多个流中提取的特征融合在体系结构的瓶颈中，并传播到生成输出本地化图的解码器网络。我们使用两种手工制作的算法，即DCT和剪接算法。我们提出的方法是在三个公共取证数据集上进行基准的，证明了针对多种竞争方法的竞争性能并实现最先进的结果，例如0.898 AUC在CASIA上。
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.
