Can we still avoid automatic face detection?
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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
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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