Research on Key Technologies of Digital Multimedia Passive Forensics

In recent years, the median filtering operation is increasingly used as a commonly used image post-processing method to hide the traces of other tampering operations. The detection of image tampering with median filtering has gradually become an important branch in the field of digital image forensics. However, the existing median filter detector field lacks a high-precision method to detect whether the image has undergone a median filter operation in terms of low-resolution images compressed with low quality factors. This paper combines the characteristics of median filter and proposes a median filter detection method based on convolutional neural network (CNN), which can automatically extract features directly from the image and build a high-precision detector. The first layer of the CNN Network in this paper adopts median filtered residuals (MFRs) of original and median filtered images. Then, the hierarchical representation is learned by alternating convolutional layers and pooling layers, and multiple features are extracted for further classification. At the same time, based on the detection of the median filter algorithm by the convolutional neural network, in order to further increase the accuracy of the algorithm detection, this paper supplements the method of wavelet high-frequency coefficients of the median filtered residual to extract the feature vector. Finally, extensive experiments show that algorithm can obtain a better performance by CNN and wavelet high-frequency coefficients algorithm, which is of importance in practical applications.