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Facilitating fashion camouflage art

Published: 21 October 2013 Publication History

Abstract

Artists and fashion designers have recently been creating a new form of art -- Camouflage Art -- which can be used to prevent computer vision algorithms from detecting faces. This digital art technique combines makeup and hair styling, or other modifications such as facial painting to help avoid automatic face-detection. In this paper, we first study the camouflage interference and its effectiveness on several current state of art techniques in face detection/recognition; and then present a tool that can facilitate digital art design for such camouflage that can fool these computer vision algorithms. This tool can find the prominent or decisive features from facial images that constitute the face being recognized; and give suggestions for camouflage options (makeup, styling, paints) on particular facial features or facial parts. Testing of this tool shows that it can effectively aid the artists or designers in creating camouflage-thwarting designs. The evaluation on suggested camouflages applied on 40 celebrities across eight different face recognition systems (both non-commercial or commercial) shows that 82.5% ~ 100% of times the subject is unrecognizable using the suggested camouflage.

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Cited By

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  • (2025)FLRNet: A bio-inspired three-stage network for Camouflaged Object Detection via filtering, localization and refinementNeurocomputing10.1016/j.neucom.2025.129523626(129523)Online publication date: Apr-2025
  • (2024)Curiosity-Driven Camouflaged Object SegmentationApplied Sciences10.3390/app1501017315:1(173)Online publication date: 28-Dec-2024
  • (2024)DCQNet: Collaborative Camouflaged Object Detection Using Cross-Sample and Cross-Scale Network2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651208(1-8)Online publication date: 30-Jun-2024
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cover image ACM Conferences
MM '13: Proceedings of the 21st ACM international conference on Multimedia
October 2013
1166 pages
ISBN:9781450324045
DOI:10.1145/2502081
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 October 2013

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Author Tags

  1. face recognition
  2. fashion camouflage art

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  • Research-article

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MM '13
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MM '13: ACM Multimedia Conference
October 21 - 25, 2013
Barcelona, Spain

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MM '13 Paper Acceptance Rate 47 of 235 submissions, 20%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all
  • (2025)FLRNet: A bio-inspired three-stage network for Camouflaged Object Detection via filtering, localization and refinementNeurocomputing10.1016/j.neucom.2025.129523626(129523)Online publication date: Apr-2025
  • (2024)Curiosity-Driven Camouflaged Object SegmentationApplied Sciences10.3390/app1501017315:1(173)Online publication date: 28-Dec-2024
  • (2024)DCQNet: Collaborative Camouflaged Object Detection Using Cross-Sample and Cross-Scale Network2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651208(1-8)Online publication date: 30-Jun-2024
  • (2023)Frequency Perception Network for Camouflaged Object DetectionProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612083(1179-1189)Online publication date: 26-Oct-2023
  • (2023)SoK: Anti-Facial Recognition Technology2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179445(864-881)Online publication date: May-2023
  • (2023)Edge-Aware Mirror Network for Camouflaged Object Detection2023 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME55011.2023.00420(2465-2470)Online publication date: Jul-2023
  • (2023)Deep Gradient Learning for Efficient Camouflaged Object DetectionMachine Intelligence Research10.1007/s11633-022-1365-920:1(92-108)Online publication date: 10-Jan-2023
  • (2022)FaceHack: Attacking Facial Recognition Systems Using Malicious Facial CharacteristicsIEEE Transactions on Biometrics, Behavior, and Identity Science10.1109/TBIOM.2021.31321324:3(361-372)Online publication date: Jul-2022
  • (2022)An Approach to Evaluate the Reliability of the Face Recognition Process Using Adversarial Samples Generated by Deep Neural NetworksIntelligent Systems and Networks10.1007/978-981-19-3394-3_28(237-245)Online publication date: 5-Jul-2022
  • (2021)Isolating Uncertainty of the Face Expression Recognition with the Meta-Learning Supervisor Neural Network2021 5th International Conference on Artificial Intelligence and Virtual Reality (AIVR)10.1145/3480433.3480447(104-112)Online publication date: 23-Jul-2021
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