Can we still avoid automatic face detection?

Research output: Contribution to journal › Conference article › Research › peer-review

Standard

Can we still avoid automatic face detection? / Wilber, Michael J.; Shmatikov, Vitaly; Belongie, Serge.

In: 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016, 23.05.2016.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Wilber, MJ, Shmatikov, V & Belongie, S 2016, 'Can we still avoid automatic face detection?', 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. https://doi.org/10.1109/WACV.2016.7477452

APA

Wilber, M. J., Shmatikov, V., & Belongie, S. (2016). Can we still avoid automatic face detection? 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. https://doi.org/10.1109/WACV.2016.7477452

Vancouver

Wilber MJ, Shmatikov V, Belongie S. Can we still avoid automatic face detection? 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. 2016 May 23. https://doi.org/10.1109/WACV.2016.7477452

Author

Wilber, Michael J. ; Shmatikov, Vitaly ; Belongie, Serge. / Can we still avoid automatic face detection?. In: 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. 2016.

Bibtex

@inproceedings{928740e168dc48d087de458ad05e3ad2,
title = "Can we still avoid automatic face detection?",
abstract = "After decades of study, automatic face detection and recognition systems are now accurate and widespread. Naturally, this means users who wish to avoid automatic recognition are becoming less able to do so. Where do we stand in this cat-and-mouse race? We currently live in a society where everyone carries a camera in their pocket. Many people willfully upload most or all of the pictures they take to social networks which invest heavily in automatic face recognition systems. In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition? If so, how? Must evasion techniques be obvious to be effective, or are there still simple measures that users can use to protect themselves? In this work, we find ways to evade face detection on Facebook, a representative example of a popular social network that uses automatic face detection to enhance their service. We challenge widely-held beliefs about evading face detection: do our old techniques such as blurring the face region or wearing {"}privacy glasses{"} still work? We show that in general, state-of-the-art detectors can often find faces even if the subject wears occluding clothing or even if the uploader damages the photo to prevent faces from being detected.",
author = "Wilber, {Michael J.} and Vitaly Shmatikov and Serge Belongie",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; IEEE Winter Conference on Applications of Computer Vision, WACV 2016 ; Conference date: 07-03-2016 Through 10-03-2016",
year = "2016",
month = may,
day = "23",
doi = "10.1109/WACV.2016.7477452",
language = "English",
journal = "2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016",

}

RIS

TY - GEN

T1 - Can we still avoid automatic face detection?

AU - Wilber, Michael J.

AU - Shmatikov, Vitaly

AU - Belongie, Serge

N1 - Publisher Copyright: © 2016 IEEE.

PY - 2016/5/23

Y1 - 2016/5/23

N2 - After decades of study, automatic face detection and recognition systems are now accurate and widespread. Naturally, this means users who wish to avoid automatic recognition are becoming less able to do so. Where do we stand in this cat-and-mouse race? We currently live in a society where everyone carries a camera in their pocket. Many people willfully upload most or all of the pictures they take to social networks which invest heavily in automatic face recognition systems. In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition? If so, how? Must evasion techniques be obvious to be effective, or are there still simple measures that users can use to protect themselves? In this work, we find ways to evade face detection on Facebook, a representative example of a popular social network that uses automatic face detection to enhance their service. We challenge widely-held beliefs about evading face detection: do our old techniques such as blurring the face region or wearing "privacy glasses" still work? We show that in general, state-of-the-art detectors can often find faces even if the subject wears occluding clothing or even if the uploader damages the photo to prevent faces from being detected.

AB - After decades of study, automatic face detection and recognition systems are now accurate and widespread. Naturally, this means users who wish to avoid automatic recognition are becoming less able to do so. Where do we stand in this cat-and-mouse race? We currently live in a society where everyone carries a camera in their pocket. Many people willfully upload most or all of the pictures they take to social networks which invest heavily in automatic face recognition systems. In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition? If so, how? Must evasion techniques be obvious to be effective, or are there still simple measures that users can use to protect themselves? In this work, we find ways to evade face detection on Facebook, a representative example of a popular social network that uses automatic face detection to enhance their service. We challenge widely-held beliefs about evading face detection: do our old techniques such as blurring the face region or wearing "privacy glasses" still work? We show that in general, state-of-the-art detectors can often find faces even if the subject wears occluding clothing or even if the uploader damages the photo to prevent faces from being detected.

UR - http://www.scopus.com/inward/record.url?scp=84977659192&partnerID=8YFLogxK

U2 - 10.1109/WACV.2016.7477452

DO - 10.1109/WACV.2016.7477452

M3 - Conference article

AN - SCOPUS:84977659192

JO - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

JF - 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

T2 - IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Y2 - 7 March 2016 through 10 March 2016

ER -

ID: 301828619