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Performance Evaluation of Transfer Learning for Pornographic Detection

  • Buddhi Ashan
  • Hyuk Cho
  • Qingzhong LiuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

The rapid growth of the Internet and online social network activities makes possible to fast share a huge volume of various digital multimedia contents such as images and videos. However, the easy accessibility of pornographic contents by general users, particularly teenagers, is problematic as well as the rare availability of the legitimate benchmark datasets for the pornographic image detection makes the research rather challenging. In this paper, we present a transfer learning approach, for which the existing general deep learning network is adopted for pornographic image detection problem. We consider five well-known deep learning models, which include VGG16, MobilNet, InceptionV3, Xception, and ResNet50, and each general deep learning network architecture is fine-turned to detect pornographic images. The experimental result with NPDI pornographic database demonstrates the effectiveness of the proposed transfer learning approach with promising detection accuracy.

Keywords

Fine-tune Transfer learning VGG16 MobileNet InceptionV3 Xception ResNet Pornographic image detection Deep learning networks 

Notes

Acknowledgements

We would like to convey our gratitude to Professor Sandra Avila and her team for letting us use the NPDI dataset in a short notice.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceSam Houston State UniversityHuntsvilleUSA

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