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Blind forgery detection using enhanced mask-region convolutional neural network

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Abstract

This work proposes a DL model for locating and classifying the forgery images. The proposed work has stages like pre-processing, feature extraction, segmentation, localization, and forgery detection. The input RGB images are converted into an YCbCr colour model in the pre-processing stage. Due to the size of the blocks, the processing time may be increased. Hence, the input images are split into overlap** blocks to reduce the time complexity. Here, a series of residual blocks are utilized for feature extraction, segmentation, and localization. Hybrid DL model Enhanced Mask-RCNN carries out this process. The Enhanced Mask-RCNN integrates the residual network with Mask-RCNN (Mask-region convolutional neural network). In this hybrid network, the residual network is used to extract the features, and the Mask-RCNN is used to segment, locate, and detect the tampered region. Further, the neural network weights are optimized by sandpiper optimization (SO) to enhance the recognition accuracy. The performance of a proposed forgery detection model is compared with three benchmark datasets and attained better accuracy of 0.991, 0.997, and 0.997 on the GRIP, Coverage, and CASIA-V1 datasets, respectively.

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Authors and Affiliations

  1. School of Engineering, Department of ECE, Mohan Babu University, Tirupati, Andhra Pradesh, 517102, India

    V. V. Satyanarayana Tallapragada

  2. Department of ECE, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, 500075, India

    D. Venkat Reddy

  3. Department of ECE, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, 500075, India

    G. V. Pradeep Kumar

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  1. V. V. Satyanarayana Tallapragada
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  2. D. Venkat Reddy
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  3. G. V. Pradeep Kumar
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Correspondence to V. V. Satyanarayana Tallapragada.

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Tallapragada, V.V.S., Reddy, D.V. & Kumar, G.V.P. Blind forgery detection using enhanced mask-region convolutional neural network. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19347-w

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