Convolutional neural network initialization approaches for image manipulation detection

Abstract

Nowadays it is common to see image forgeries on almost every media in both professional and personal contexts. From visual retouches to deliberate fake scenes, the technology used to create image forgeries gets easier to use for all users. At the same time, different techniques to assess the authenticity of the content of an image have appeared by taking advantage of the deep learning paradigm. In this paper we propose two initialization approaches for Convolutional Neural Networks (CNNs) used for the detection of image manipulation operations. We focus on the variance stability for the output of a convolutional filter in CNN. Our first proposal is a scaling approach for first-layer convolutional kernels which can cope well with filters generated by different algorithms. Our second proposal is a random high-pass filter initialization approach for CNN's first convolutional layer. The first proposal explicitly computes simple statistical properties of the input signal, while the second approach incorporates the consideration of input statistics in the filter derivation without the need of carrying out explicit computation on the input. Experimental results show the utility of both approaches with improved performance in different image manipulation detection problems and on different CNN architectures.