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Copy move and splicing forgery detection using deep convolution neural network, and semantic segmentation

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Abstract

Image forgeries can be detected and localized by using deep convolution neural network, and semantic segmentation. Color illumination is used to apply color map after pre-processing step. To train VGG-16 with two classes using deep convolution neural network transfer learning approach is used. This algorithm classifies image’s pixels having a forgery or not. These classified images with color pixel label are trained using semantic segmentation to localize forged pixels. These algorithms are tested on GRIP, DVMM, CMFD, and BSDS300 datasets. All these images are divided into two folders. One folder contains all forged images, and another folder contains labels of forged pixels. The experiment result shows that total accuracy is 0.98482, average accuracy is 0.98581, average IoU is 0.91148, weighted IoU is 0.97193, and average boundary F1 score is 0.86404. The forged pixel accuracy is 0.98698, IoU of the forged pixel is 0.83945, and average boundary F1 score of the forged image is 0.79709. Not Forged pixel accuracy is 0.98463, IoU of not forged pixel is 0.98351 and average boundary F1 score of not forged image is 0.93055. The experiment results show that forged pixel and not forged detection accuracy is above 98%, which is best among other methods.

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  1. Electronics and Communication Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India

    Abhishek & Neeru Jindal

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Abhishek, Jindal, N. Copy move and splicing forgery detection using deep convolution neural network, and semantic segmentation. Multimed Tools Appl 80, 3571–3599 (2021). https://doi.org/10.1007/s11042-020-09816-3

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