A simple and effective initialization of CNN for forensics of image processing operations
Résumé
In this paper we present a simple yet effective initialization method for convolutional neural networks (CNNs). The proposed method extends the well-known Xavier initialization and can cope well with CNNs used for forensic detection of image processing operations. Our initialization inherits the simplicity and advantages of the Xavier initialization, and the difference is that our method generates a set of high-pass filters for the initialization of CNN's first layer. This allows us to better identify forensic traces which usually lie towards the high-frequency part of the image. We test the proposed method with two CNNs for two forensic problems, i.e., a multiclass classification problem of a group of image processing operations and a median filtering forensic problem with JPEG post-processing. Experimental results show the utility of our initialization.