September 1, 2011
NEW CV Dazzle Looks N°6, N°7. Developed for Designs for a Different Future 2020 to break convolutional neural network face recognition. © Adam Harvey 2020.
CV Dazzle # CV Dazzle is a form of camouflage from computer vision created in 2010 as my masters thesis at New York University’s Interactive Telecommunications Program. Unlike traditional camouflage, such as disruptive-pattern material, that hides the wearer from human observation, CV Dazzle is designed to break machine vision systems while still remaining perceptible to human observers.
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November 1, 2018
Exposing.ai project
EXPOSING.ai # Exposing.ai is a research project about the origins and endpoints of biometric image datasets created “in the wild”. The project investigates how photos have unwittingly become part of an information supply chain powering the global biometrics industry.
Research from the Exposing.ai project hsa been featured in the Financial Times, New York Times, Nature, a US Government Accountability Report, the 2020 AI Index, several academic research papers, and has helped pushed forward an urgent discussion about the ethics of dataset collection into public discourse.
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March 1, 2017
HyperFace by Adam Harvey for Hyphen-Labs. Model: Ashley Baccus-Clark. Photo: © Hyphen-Labs and Adam Harvey / hyphen-labs.com. 2017
HyperFace # False-face computer vision camouflage patterns designed and developed for Hyphen Labs’ NeuroSpeculative AfroFeminism at Sundance Film Festival 2017.
Adam Harvey x Hyphen-Labs # The HyperFace (Version 1) prototype was developed for Hyphen-Labs NeuroSpeculative AfroFeminism project and debuted at the Sundance Film Festival in 2017. The project was collaboration with Hyphen Labs members Ashley Baccus-Clark, Carmen Aguilar y Wedge, Ece Tankal, Nitzan Bartov, and JB Rubinovitz.
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November 1, 2018
Face redaction demo with the DFACE app. Detection made using 1.18sm model.
DFACE.app # Automatic, private, open-source face redaction web app: try it here DFACE.app
DFACE uses the YOLOV5 neural network object detection framework to run face detection in a web browser so photos never leave a user’s device. It can process up to 1,000 faces per image at down to 10x10 pixels per face with varying effects (color fill, blur, or emoji), and supports batch-processing multiple images.
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