Digital images have become a dominant source of information and means of communication in our society. However, they can easily be altered using readily available image editing tools. In this paper, we propose a new blind image forgery detection technique which employs a new backbone architecture for deep learning which is called ResNet-conv. ResNet-conv is obtained by replacing the feature pyramid network in ResNet-FPN with a set of convolutional layers. This new backbone is used to generate the initial feature map which is then to train the Mask-RCNN to generate masks for spliced regions in forged images. The proposed network is specifically designed to learn discriminative artifacts from tampered regions. Two different ResNet architectures are considered, namely ResNet-50 and ResNet-101. The ImageNet, He_normal, and Xavier_normal initialization techniques are employed and compared based on convergence. To train a robust model for this architecture, several post-processing techniques are applied to the input images. The proposed network is trained and evaluated using a computer-generated image splicing dataset and found to be more efficient than other techniques.

中文翻译：

---

使用mask-RCNN的图像拼接检测

数字图像已成为我们社会的主要信息来源和通信手段。但是，可以使用现成的图像编辑工具轻松更改它们。在本文中，我们提出了一种新的盲图像伪造检测技术，该技术采用称为 ResNet-conv 的新型深度学习骨干架构。ResNet-conv 是通过用一组卷积层替换 ResNet-FPN 中的特征金字塔网络获得的。这个新的主干用于生成初始特征图，然后训练 Mask-RCNN 为伪造图像中的拼接区域生成掩码。所提出的网络专门设计用于从篡改区域学习判别性工件。考虑了两种不同的 ResNet 架构，即 ResNet-50 和 ResNet-101。ImageNet，He_normal，采用和 Xavier_normal 初始化技术并基于收敛进行比较。为了为这种架构训练一个健壮的模型，对输入图像应用了几种后处理技术。所提出的网络使用计算机生成的图像拼接数据集进行训练和评估，发现比其他技术更有效。