Computer Science And Technology - Research Publications

Permanent URI for this collectionhttps://kr.cup.edu.in/handle/32116/82

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Author: search.filters.author.Khurana, Surinder SinghSubject: search.filters.subject.Deep learning

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    An efficient approach for copy-move image forgery detection using convolution neural network
    (Springer, 2022-02-17T00:00:00) Koul, Saboor; Kumar, Munish; Khurana, Surinder Singh; Mushtaq, Faisel; Kumar, Krishan
    Digital imaging has become elementary in this novel era of technology with unconventional image forging techniques and tools. Since, we understand that digital image forgery is possible, it cannot be even presented as a piece of evidence anywhere. Dissecting this fact, we must dig unfathomable into the issue to help alleviate such derelictions. Copy-move and splicing of images to create a forged one prevail in this monarchy of digitalization. Copy-move involves copying one part of the image and pasting it to another part of the image while the latter involves merging of two images to significantly change the original image and create a new forged one. In this article, a novel slant using a convolutional neural network (CNN) has been proposed for automatic detection of copy-move forgery detection. For the experimental work, a benchmark dataset namely, MICC-F2000 is considered which consists of 2000 images in which 1300 are original and 700 are forged. The experimental results depict that the proposed model outperforms the other traditional methods for copy-move forgery detection. The results of copy-move forgery were highly promising with an accuracy of 97.52% which is 2.52% higher than the existing methods. � 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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    UrduDeepNet: offline handwritten Urdu character recognition using deep neural network
    (Springer Science and Business Media Deutschland GmbH, 2021-06-07T00:00:00) Mushtaq, Faisel; Misgar, Muzafar Mehraj; Kumar, Munish; Khurana, Surinder Singh
    Handwritten Urdu character recognition system faces several challenges including the writer-dependent variations and non-availability of benchmark databases for cursive writing scripts. In this study, we propose a handwritten Urdu character dataset for Nasta�liq writing style covering isolated, positional characters as well as numerals. We also propose a convolutional neural network (CNN) architecture for the recognition of handwritten Urdu characters and numerals. CNN is a novel technique for image recognition that does not need explicit feature engineering and extraction and produces efficient results as compared to standard handcrafted feature extraction approaches. The proposed system was trained on a training dataset of 74, 285 samples and evaluated on a test dataset of 21, 223 samples and achieved a recognition rate of 98.82% for 133 classes, outperforming the results of all state-of-the-art systems for the Urdu language. � 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.