With the rapid development of splicing manipulation, more and more negative effects have been brought. Therefore, the demand for image splicing detection algorithms is growing dramatically. In this paper, a new image splicing detection method is proposed which is based on convolutional neural network (CNN) with weight combination strategy. In the proposed method, three types of features are selected to distinguish splicing manipulation including YCbCr features, edge features and photo response non-uniformity (PRNU) features, which are combined according to weight by the combination strategy. Different from the other methods, these weight parameters are automatically adjusted during the CNN training process, until the best ratio is obtained. Experiments show that the proposed method has higher accuracy than the other methods using CNN, and the depth of the CNN in the method proposed is much less than the compared methods.

随着拼接操作的迅速发展，带来了越来越多的负面影响。因此，对图像拼接检测算法的需求急剧增长。提出了一种基于卷积神经网络（CNN）和加权组合策略的图像拼接检测新方法。在提出的方法中，选择了三种类型的特征来区分拼接操作，包括YCbCr特征，边缘特征和光响应非均匀性（PRNU）特征，这些特征根据权重通过组合策略进行组合。与其他方法不同，在CNN训练过程中会自动调整这些权重参数，直到获得最佳比例。实验表明，该方法比使用CNN的其他方法具有更高的准确性，