Recently, many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these detection methods focus on the local patches or local objects. In fact, image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. However, some specific image contents are hardly retained by CNN-based detection networks, but if included, would improve the detection accuracy of the networks. To resolve these issues, we propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network. This dual-encoder is followed by a spatial pyramid global-feature extraction module that expands the global insight of D-Unet for classifying the tampered and non-tampered regions more accurately. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection, without requiring pre-training or training on a large number of forgery images. Moreover, it was stably robust to different attacks.

翻译：最近,基于连锁神经网络(CNNs)的许多探测方法被提议用于图像复制伪造检测。这些检测方法大多侧重于本地补丁或本地对象。事实上,图像拼字伪造检测是一项全球性的二进制分类任务,通过图像指纹区分被篡改的区域和非标书区域。然而,某些具体的图像内容几乎没有被CNN的检测网络所保留,但如果包含,则会提高网络的检测准确性。为了解决这些问题,我们建议建立一个名为双读U-Net(D-Unet)的新网络,用于图像复制伪造检测,其中使用一个未固定的编码器和固定的编码器。未固定的编码器自主地学习了区分被篡改区域和非标书区域之间的图像指纹,而固定的编码器有意提供有助于网络学习和探测的定向信息。为了解决这些问题,我们建议建立一个名为双级的双编码全球加密提取模块,以扩大D-Unet对全球图像拼图学的洞度,以不固定的编码和不精确的图像测试方法来分类。