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Detecting Digital Photo Tampering with Deep Learning

  • Mitchell B. Singleton
  • Qingzhong Liu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 251)

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.

Keywords

Photography Detection of tampering Detection of modification Detection of falsification Deep learning 

Notes

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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Copyright information

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

Authors and Affiliations

  1. 1.Department of Computer ScienceSam Houston State UniversityHuntsvilleUSA

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