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Efficacy of Residual Methods for Passive Image Forensics Using Four Filtered Residue CNN

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Abstract

Digital images can be manipulated easily due to the availability of various editing tools. These editing tools are employed to preserve the original content of an image. However, editing tools also leave the traces of the manipulations on an image. Many different detectors have been proposed to identify these traces, but these detectors are only capable of detecting specific manipulation types. In addition, some general purpose detectors are also available, but their performance is best suited for uncompressed images of large resolution. In this article, a general purpose, Four filtered residue–convolutional neural network (FFR–CNN) is proposed to detect multiple manipulations, mainly for low resolution and JPEG compressed images. The volumetric input for three dimensional FFR–CNN is constructed by stacking along the depth the four residuals obtained using average, Gaussian, Laplacian and median filtering. The four residuals reveal various manipulations in terms of background and texture. Thus, the quality of the four residuals is combined to enhance the performance of the proposed network. The performance of the FFR–CNN is evaluated for the detection of different manipulations (Gaussian blurring, additive white Gaussian Noise, median filtering, re-scaling and rotation) against the state-of-the art detectors. The generalization ability and high detection accuracy in the presence of anti-forensics operation highlight the efficacy of the proposed FFR–CNN. Furthermore, constrained time complexity supports the effectiveness of FFR–CNN for real time applications. For conducting all the experiments, the training–testing ratio is 85:15, e.g., 85% data are used for training and remaining 15% data are used for testing purpose.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, 201309, India

    Aanchal Agarwal

  2. RBS Services India Private Limited, NatWest Group, Candor Techspace, Sector-21, Dundahera, Gurugram, Haryana, 122016, India

    Abhinav Gupta

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Agarwal, A., Gupta, A. Efficacy of Residual Methods for Passive Image Forensics Using Four Filtered Residue CNN. SN COMPUT. SCI. 3, 491 (2022). https://doi.org/10.1007/s42979-022-01396-3

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