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International Conference on Machine Learning and Intelligent Communications

MLICOM 2018: Machine Learning and Intelligent Communications pp 480–489Cite as

Detecting Digital Photo Tampering with Deep Learning

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Abstract

The tools to tamper with digital photographs have become easier to use by the lay person and techniques have evolved that make detection of tampering more difficult, there is a clear need to not only discover novel ways to distinguish the difference between real and fake, but also to make the detection quicker and easier. Using content-aware resizing, which is also known as image retargeting, seam carving, content-aware rescaling, liquid rescaling, or liquid resizing, allows for changing the resolution of an image while keeping the content unchanged in the image. In this paper, the retraining of Inception ResNet v2, one of the best object detection deep learning classifiers, is undertaken to look at classifications of untampered versus tampered photos via seam removal.

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Acknowledgments

We highly appreciate the support for this study from the National Science Foundation under Award #1318688 and from the SHSU Office of Research and Sponsored Programs under an Enhanced Research Grant.

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Authors and Affiliations

  1. Department of Computer Science, Sam Houston State University, Huntsville, TX, 77341, USA

    Mitchell B. Singleton & Qingzhong Liu

Authors
  1. Mitchell B. Singleton
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  2. Qingzhong Liu
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Corresponding author

Correspondence to Qingzhong Liu .

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Editors and Affiliations

  1. Zhejiang University of Technology, Hangzhou Shi, China

    Limin Meng

  2. Department of Networks, Simula Research Laboratory, Oslo, Norway

    Yan Zhang

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Singleton, M.B., Liu, Q. (2018). Detecting Digital Photo Tampering with Deep Learning. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_47

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  • DOI: https://doi.org/10.1007/978-3-030-00557-3_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00556-6

  • Online ISBN: 978-3-030-00557-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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