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A Web-Based Service for Disturbing Image Detection

  • Markos Zampoglou
  • Symeon Papadopoulos
  • Yiannis Kompatsiaris
  • Jochen Spangenberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10133)

Abstract

As User Generated Content takes up an increasing share of the total Internet multimedia traffic, it becomes increasingly important to protect users (be they consumers or professionals, such as journalists) from potentially traumatizing content that is accessible on the web. In this demonstration, we present a web service that can identify disturbing or graphic content in images. The service can be used by platforms for filtering or to warn users prior to exposing them to such content. We evaluate the performance of the service and propose solutions towards extending the training dataset and thus further improving the performance of the service, while minimizing emotional distress to human annotators.

Keywords

Convolutional Neural Network Transfer Learning User Generate Content Rest Service Convolutional Neural Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is supported by the REVEAL and InVID projects, partially funded by the European Commission under contract numbers 610928 and 687786. In addition, we would like to acknowledge the support that NVIDIA provided us through the GPU Grant Program.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Markos Zampoglou
    • 1
  • Symeon Papadopoulos
    • 1
  • Yiannis Kompatsiaris
    • 1
  • Jochen Spangenberg
    • 2
  1. 1.CERTH-ITIThessalonikiGreece
  2. 2.Deutsche WelleBerlinGermany

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