On the Use of "Deep" Features for Online Image Sharing

@inproceedings{Tonge2018OnTU,
  title={On the Use of "Deep" Features for Online Image Sharing},
  author={Ashwini Tonge and Cornelia Caragea},
  booktitle={WWW},
  year={2018}
}
Online image sharing in social networking sites such as Facebook, Flickr, and Instagram can lead to unwanted disclosure and privacy violations, when privacy settings are used inappropriately. Despite that social networking sites allow users to set their privacy preferences, this can be cumbersome for the vast majority of users. In this paper, we explore privacy prediction models for social media that can automatically identify private (or sensitive) content from images, before they are shared… CONTINUE READING

Figures, Tables, and Topics from this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 23 REFERENCES

Distinctive Image Features from Scale-Invariant Keypoints

  • International Journal of Computer Vision
  • 2004
VIEW 12 EXCERPTS
HIGHLY INFLUENTIAL

Privacyaware image classification and search

Sergej Zerr, Stefan Siersdorfer, JonathonHare, Elena Demidova
  • In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. ACM,
  • 2012
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

  • International Journal of Computer Vision
  • 2001
VIEW 8 EXCERPTS
HIGHLY INFLUENTIAL

Online; accessed 12-April-2017

Yann LeCun
  • Facebook Envisions AI That Keeps You From Uploading Embarrassing Pics. https://www.wired.com/2014/12/fb/all/1
  • 2017
VIEW 1 EXCERPT

Similar Papers

Loading similar papers…