Multiscale Content-Independent Feature Fusion Network for Source Camera Identification

Abstract

In recent years, source camera identification has become a research hotspot in the field of image forensics and has received increasing attention. It has high application value in combating the spread of pornographic photos, copyright authentication of art photos, image tampering forensics, and so on. Although the existing algorithms greatly promote the research progress of source camera identification, they still cannot effectively reduce the interference of image content with image forensics. To suppress the influence of image content on source camera identification, a multiscale content-independent feature fusion network (MCIFFN) is proposed to solve the problem of source camera identification. MCIFFN is composed of three parallel branch networks. Before the image is sent to the first two branch networks, an adaptive filtering module is needed to filter the image content and extract the noise features, and then the noise features are sent to the corresponding convolutional neural networks (CNN), respectively. In order to retain the information related to the image color, this paper does not preprocess the third branch network, but directly sends the image data to CNN. Finally, the content-independent features of different scales extracted from the three branch networks are fused, and the fused features are used for image source identification. The CNN feature extraction network in MCIFFN is a shallow network embedded with a squeeze and exception (SE) structure called SE-SCINet. The experimental results show that the proposed MCIFFN is effective and robust, and the classification accuracy is improved by approximately 2% compared with the SE-SCINet network.

Smartphone Camera Identification from Low-Mid Frequency DCT Coefficients of Dark Images

“…Source camera identification based on content-adaptive fusion network is discussed in [ 50 ]. A multiscale content-independent feature fusion network (MCIFFN) is proposed in [ 51 ]. A significant number of works employ convolutional neural networks (CNN) for device identification, e.g., [ 18 , 52 , 53 , 54 ].…”

Source Camera Identification with a Robust Device Fingerprint: Evolution from Image-Based to Video-Based Approaches

“…In recent years, there has been a notable shift towards a data-driven approach in source camera identification, leveraging Convolutional Neural Networks (CNNs) to learn camera-specific features that map images to their respective source cameras. While significant progress has been made in utilizing deep learning techniques for camera model identification [17][18][19][20][21][22][23][24][25][26][27][28], several challenges still demand attention. One such challenge involves differentiating between devices of the same model, which is crucial for achieving precise traceability.…”