TY - GEN
T1 - Privacy Protection Against Automated Tracking System Using Adversarial Patch
AU - Takiwaki, Hiroto
AU - Kuribayashi, Minoru
AU - Funabiki, Nobuo
AU - Rava, Mehul S.
N1 - Funding Information:
ACKNOWLEDGMENT This research was supported by the JSPS KAKENHI Grant Number 19K22846 and 22K19777, JST SICORP Grant Number JPMJSC20C3, and JST CREST Grant Number JP-MJCR20D3, Japan.
Publisher Copyright:
© 2022 Asia-Pacific of Signal and Information Processing Association (APSIPA).
PY - 2022
Y1 - 2022
N2 - Advancesin machine learning technologies, such as convolutional neural networks, have helped identify individuals using face recognition and identification techniques. A system can be constructed to detect the presence of specific features in an object. However, if the technologies are abused, individuals can be tracked automatically and their privacy would be violated. Therefore, it is necessary to develop a technique for avoiding automated human tracking systems that use facial identification. Conventional methods study adversarial noise to avoid recognition and face identification. However, they do not investigate the geometrical changes in the patch area. Here, we compared the performance of a non-transparent patch with that of a transparent patch and proposed a method for improving robustness against changes in position. Our experiments demonstrated that the non-transparent patch does not significantly affect the success rate of a face-identification system. The proposed method improves robustness against changes in the patch position.
AB - Advancesin machine learning technologies, such as convolutional neural networks, have helped identify individuals using face recognition and identification techniques. A system can be constructed to detect the presence of specific features in an object. However, if the technologies are abused, individuals can be tracked automatically and their privacy would be violated. Therefore, it is necessary to develop a technique for avoiding automated human tracking systems that use facial identification. Conventional methods study adversarial noise to avoid recognition and face identification. However, they do not investigate the geometrical changes in the patch area. Here, we compared the performance of a non-transparent patch with that of a transparent patch and proposed a method for improving robustness against changes in position. Our experiments demonstrated that the non-transparent patch does not significantly affect the success rate of a face-identification system. The proposed method improves robustness against changes in the patch position.
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U2 - 10.23919/APSIPAASC55919.2022.9980065
DO - 10.23919/APSIPAASC55919.2022.9980065
M3 - Conference contribution
AN - SCOPUS:85146268115
T3 - Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
SP - 1849
EP - 1854
BT - Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022
Y2 - 7 November 2022 through 10 November 2022
ER -