This study proposes a novel deep learning-based method which can detect different types of image editing operations carried out on images. Unlike most of the existing methods, which can only detect the editing operations considered in the training stage, the proposed method can generalise to manipulations not seen in the training stage. The method is based on the classification of image pairs as either similarly or differently processed using a deep siamese neural network. Once the network learns features that can discriminate different editing operations, it can check whether an image is processed with an editing operation, not present in the training stage, using the one-shot classification strategy. An image forgery detection and localisation technique is also proposed using the trained siamese network. The experimental results show the efficacy of the proposed method in detecting different editing operations and also show the ability in detecting and localising image forgeries.

中文翻译：

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基于暹罗卷积神经网络的通用图像取证方法

这项研究提出了一种新颖的基于深度学习的方法，该方法可以检测对图像执行的不同类型的图像编辑操作。与大多数现有方法只能检测训练阶段中考虑的编辑操作不同，所提出的方法可以推广到训练阶段中看不到的操作。该方法基于使用深度暹罗神经网络进行相似或不同处理的图像对的分类。网络学习到可以区分不同编辑操作的功能后，就可以检查图像是否已通过训练阶段未使用的编辑操作进行处理。一次性分类战略。还使用经过训练的暹罗网络提出了图像伪造检测和定位技术。实验结果表明了该方法在检测不同编辑操作方面的有效性，并且还具有检测和定位图像伪造的能力。