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Binary Pattern for Copy-Move Image Forgery Detection

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Machine Vision and Augmented Intelligence

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1007))

Abstract

To identify copy-move image counterfeiting, this work proposes a new feature extraction approach based on a modified Gabor filter and a Center Symmetric Local Binary Pattern (CSLBP). The input image is first pre-processed, and then Gabor filter and CSLBP feature extraction are performed to the image with various scales and orientations. The Manhattan distance is used to detect forged regions by comparing the critical spots. To classify the counterfeit photos, Hybrid Neural Networks with Decision Tree (HNN-DT) is used on feature extraction. The performance of the proposed Modified Gabor filter with CSLBP is compared to that of existing feature extraction methods such as the Improved Speeded-up Robust Features (SURF) algorithm with PCA and the existing Improved Speeded-up Robust Features (SURF) algorithm with PCA. Using the Gabor filter and CSLBP, the expected results demonstrate efficient classification.

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Author information

Authors and Affiliations

  1. Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, India

    Neeraj Rathore & Pawan Singh

  2. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Neelesh Jain

  3. Central University Rajasthan, Ajmer, India

    Pawan Singh

Authors
  1. Neeraj Rathore
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  2. Neelesh Jain
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  3. Pawan Singh
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Corresponding author

Correspondence to Neeraj Rathore .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Jamshedpur, Jamshedpur, India

    Koushlendra Kumar Singh

  2. Department of Computer Science and Engineering, IIITDM Jabalpur, Jabalpur, India

    Manish Kumar Bajpai

  3. Leeds Beckett University, Leeds, UK

    Akbar Sheikh Akbari

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Rathore, N., Jain, N., Singh, P. (2023). Binary Pattern for Copy-Move Image Forgery Detection. In: Kumar Singh, K., Bajpai, M.K., Sheikh Akbari, A. (eds) Machine Vision and Augmented Intelligence. Lecture Notes in Electrical Engineering, vol 1007. Springer, Singapore. https://doi.org/10.1007/978-981-99-0189-0_37

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  • DOI: https://doi.org/10.1007/978-981-99-0189-0_37

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  • Online ISBN: 978-981-99-0189-0

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