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A Deep Learning-Based Method for Image Tampering Detection

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1500))

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Abstract

This paper proposes a method to detect the fake objects in images by combining You Only Look Once (YOLO) to define the objects and image post-processing techniques to authenticate the forgery manipulations on objects. YOLO is developed in 2016 and improved in different versions. For the object detection in images, authors used the first version of YOLO. In the proposed method, YOLO is firstly used to identify objects on the image and these objects are removed from the background to reduce computational complexity. The boundaries of objects will be then detected, and their sharpness distribution are calculated as the traces for determining the object tampering. The objects with higher sharpness at the boundaries will be compared the objects in the same group. If there is an object’s features similarity, there will be a copy-move object; otherwise, a spliced object. These steps are integrated into a neural network model trained by both spliced and copy-move image datasets. The combination of YOLO and the proposed neural network model has solved the problem of detecting fake objects with average accuracy of 95.3% for copy-move images and 93.8% for spliced images. The proposed method can be efficient not only for copy-move images and spliced images, but also for the mixed images.

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Author information

Authors and Affiliations

  1. International University, Ho Chi Minh City, Vietnam

    Kha-Tu Huynh & Tu-Nga Ly

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Kha-Tu Huynh, Tu-Nga Ly & Thuong Le-Tien

  3. University of Technology, Ho Chi Minh City, Vietnam

    Thuong Le-Tien

Authors
  1. Kha-Tu Huynh
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  2. Tu-Nga Ly
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  3. Thuong Le-Tien
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Corresponding author

Correspondence to Kha-Tu Huynh .

Editor information

Editors and Affiliations

  1. HCMC University of Technology (HCMUT), Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

  4. Hosei University, Tokyo, Japan

    Makoto Takizawa

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Huynh, KT., Ly, TN., Le-Tien, T. (2021). A Deep Learning-Based Method for Image Tampering Detection. In: Dang, T.K., Küng, J., Chung, T.M., Takizawa, M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2021. Communications in Computer and Information Science, vol 1500. Springer, Singapore. https://doi.org/10.1007/978-981-16-8062-5_11

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  • DOI: https://doi.org/10.1007/978-981-16-8062-5_11

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  • Online ISBN: 978-981-16-8062-5

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