The emergence of powerful image editing software has substantially facilitated digital image tampering, leading to many security issues. Hence, it is urgent to identify tampered images and localize tampered regions. Although much attention has been devoted to image tampering localization in recent years, it is still challenging to perform tampering localization in practical forensic applications. The reasons include the difficulty of learning discriminative representations of tampering traces and the lack of realistic tampered images for training. Since Photoshop is widely used for image tampering in practice, this paper attempts to address the issue of tampering localization by focusing on the detection of commonly used editing tools and operations in Photoshop. In order to well capture tampering traces, a fully convolutional encoder-decoder architecture is designed, where dense connections and dilated convolutions are adopted for achieving better localization performance. In order to effectively train a model in the case of insufficient tampered images, we design a training data generation strategy by resorting to Photoshop scripting, which can imitate human manipulations and generate large-scale training samples. Extensive experimental results show that the proposed approach outperforms state-of-the-art competitors when the model is trained with only generated images or fine-tuned with a small amount of realistic tampered images. The proposed method also has good robustness against some common post-processing operations.

中文翻译：

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使用密集全卷积网络进行图像篡改定位


强大的图像编辑软件的出现极大地方便了数字图像篡改，从而导致许多安全问题。因此，迫切需要识别篡改图像并定位篡改区域。尽管近年来图像篡改定位受到了很多关注，但在实际取证应用中进行篡改定位仍然具有挑战性。原因包括难以学习篡改痕迹的辨别性表示以及缺乏用于训练的真实篡改图像。由于Photoshop在实践中广泛用于图像篡改，本文试图通过重点检测Photoshop中常用的编辑工具和操作来解决篡改定位问题。为了很好地捕获篡改痕迹，设计了一种全卷积编码器-解码器架构，其中采用密集连接和扩张卷积来实现更好的定位性能。为了在篡改图像不足的情况下有效地训练模型，我们利用Photoshop脚本设计了训练数据生成策略，可以模仿人类操作并生成大规模训练样本。大量的实验结果表明，当模型仅使用生成的图像进行训练或使用少量真实的篡改图像进行微调时，所提出的方法优于最先进的竞争对手。所提出的方法对于一些常见的后处理操作也具有良好的鲁棒性。