In this work, we present a deep learning-based approach for image tampering localization fusion. This approach is designed to combine the outcomes of multiple image forensics algorithms and provides a fused tampering localization map, which requires no expert knowledge and is easier to interpret by end users. Our fusion framework includes a set of five individual tampering localization methods for splicing localization on JPEG images. The proposed deep learning fusion model is an adapted architecture, initially proposed for the image restoration task, that performs multiple operations in parallel, weighted by an attention mechanism to enable the selection of proper operations depending on the input signals. This weighting process can be very beneficial for cases where the input signal is very diverse, as in our case where the output signals of multiple image forensics algorithms are combined. Evaluation in three publicly available forensics datasets demonstrates that the performance of the proposed approach is competitive, outperforming the individual forensics techniques as well as another recently proposed fusion framework in the majority of cases.

翻译：在这项工作中,我们展示了一种深层次的基于学习的图象篡改本地化的方法。这个方法旨在将多种图像法证算法的结果结合起来,并提供一个精密的篡改本地化图,不需要专家知识,而且更容易由终端用户解释。我们的集成框架包括一套五种个人篡改本地化方法,用于在JPEG图像上拼接本地化。提议的深层学习混合模型是一个经过调整的结构,最初是为图像恢复任务而提议的,它同时进行多种操作,并辅之以一个关注机制,以便能够根据输入信号选择适当的操作。这种加权过程对于输入信号非常多样化的情况非常有益,例如我们把多种图像法证算法的输出信号合并在一起的情况。对三种公开提供的法医数据集的评价表明,拟议方法的绩效是竞争性的,优于个人法证技术以及最近提出的大多数案例的合并框架。