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.
Highlights
With the rapid development of the new generation of information technology represented by the Internet, big data and artificial intelligence, networking, digitization and intellectualization have become the trend of the times, and digital images have been integrated into all aspects of social life
Source camera identification is an important part of digital image forensics, which works to determine from which camera a digital image originated
multiscale content-independent feature fusion network (MCIFFN)-1 is the top branch replaced by the MCIFFN network in Figure 2, MCIFFN-2 is the middle branch of the MCIFFN network, MCIFFN-3 is the bottom branch replaced by the MCIFFN network in Figure 2, MCIFFN-F3 is the network in which the sizes of all filters of the preprocessing module in the MCIFFN are 3 × 3, MCIFFN-F5 is the network in which the sizes of all filters of the preprocessing module in the MCIFFN are 5 × 5, MCIFFN-1-2 represents the converged network of branches 1 and 2 of the MCIFFN network and MCIFFN-NoRes is the network that removes the identity mapping between the 3 × 3 and 5 × 5 filters of the MCIFFN network
Summary
With the rapid development of the new generation of information technology represented by the Internet, big data and artificial intelligence, networking, digitization and intellectualization have become the trend of the times, and digital images have been integrated into all aspects of social life. A large number of algorithms for source camera identification has emerged Their principles and methods are different, they all have one thing in common: to extract some traces introduced by human or equipment defects in the image-shooting process and determine the image acquisition equipment according to these traces. In [7], the authors present source camera identification via image texture features that are extracted from well-selected color models and color channels, and the proposed method is superior in both detection accuracy and robustness than the other methods. Through a detailed imaging model and its component analysis, the method presented in [14] estimated the intrinsic fingerprint of various camera processing operations Another category of source camera identification methods is based on deep learning, which uses CNNs to automatically extract useful features and classify them using classifiers.
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