In recent years, researchers have attempted to explore methods for real-time image forgery detection. Many approaches were developed to detect a certain number of image modification methods. There are many limitations in practical application. In this paper, a multi-domain learning convolutional neural network (MDL-CNN) is proposed to overcome this limitation. We extract the periodicity property from the original and modified image. Features of modified image extracted from different datasets are then fed into the neural network in training process. Since the proposed MDL-CNN is trained by different types of tempering datasets, our method can distinguish many types of image modifications. To decrease the computation of proposed scheme, 1 × 1 kernel convolution layer is used in the second convolutional layer of each network. Furthermore, a multi-domain loss function is developed to enhance the recognition ability of in-depth learning features. Experimental evaluation results show that MDL-CNN method can significantly improve the forensic performance.

中文翻译：

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基于多领域学习的实时图像取证方案

近年来，研究人员已尝试探索用于实时图像伪造检测的方法。开发了许多方法来检测一定数量的图像修改方法。在实际应用中有很多限制。本文提出了一种多域学习卷积神经网络（MDL-CNN）来克服这一限制。我们从原始图像和修改后的图像中提取周期性属性。从不同数据集中提取的修改图像的特征然后在训练过程中输入到神经网络。由于所提出的MDL-CNN受不同类型的回火数据集训练，因此我们的方法可以区分许多类型的图像修改。为了减少建议方案的计算量，每个网络的第二个卷积层中使用了1×1内核卷积层。此外，开发了多域丢失功能以增强深度学习功能的识别能力。实验评估结果表明，MDL-CNN方法可以显着提高取证性能。