Fashion

CV Dazzle

September 1, 2011
Fashion, Counter-Surveillance, Face-Detection, Computer Vision

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.

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. It is the first documented camouflage technique to successfully attack a computer vision algorithm.

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HyperFace

March 1, 2017
Computer Vision, Face-Detection, Camouflage, Fashion, Counter-Surveillance

HyperFace by Adam Harvey for Hyphen-Labs. Model: Ashley Baccus-Clark. Photo: © Hyphen-Labs and Adam Harvey / hyphen-labs.com. 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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Stealth Wear

April 1, 2017
Drones, Counter-Surveillance, Fashion

© Adam Harvey

© Adam Harvey

Stealth Wear #

Collectively, Stealth Wear is a vision for fashion that addresses the rise of surveillance, the power of those who surveil, and the growing need to exert more control over privacy.

Building off previous work with CV Dazzle, camouflage from face detection, Stealth Wear continues to explore the aesthetics of privacy and the potential for fashion to challenge authoritarian surveillance technologies.

The collection is inspired by traditional Islamic dress and the idea that garments can provide a separation between man and God. In Stealth Wear, this idea is re-imagined in the context of drone warfare as garments that provide a separation between man and Drone. Items are fabricated with silver-plated fabric that reflects thermal radiation, enabling the wearer to avert overhead thermal surveillance.

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MegaPixels

November 1, 2017
Computer Vision, Face-Recognition, Camouflage, Fashion, Counter-Surveillance

A user searches for their identity in the MegaFace dataset at Glass Room exhibition in London. Photo ©  Tactical Tech.

A user searches for their identity in the MegaFace dataset at Glass Room exhibition in London. Photo © Tactical Tech.

MegaPixels: GlassRoom #

MegaPixels is an ongoing project about machine learning image datasets. This first chapter of the project launched in London in 2017 in collaboration with Tactical Tech for the Glass Room exhibition.

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CamoFlash

September 28, 2014
Counter-Surveillance, Fashion

Anti-Paparazzi Clutch Prototype demonstrated by Tiam Taheri in Brooklyn 2016. Photo © Adam Harvey

Anti-Paparazzi Clutch Prototype demonstrated by Tiam Taheri in Brooklyn 2016. Photo © Adam Harvey

CamoFlash #

The anti-paparazzi clutch. A high-power LED device to overexpose professional dSLR cameras with no delay. This project is a prototype and case study for wearable technology development.

This project began as a conceptual prototype at NYU ITP in 2008 then developed into functional prototype in 2010. The current prototype is capable of thwarting dSLR-based paparazzi attacks within 10 ft. It works by detecting a camera flash (xenon bulb only) and responding in less than .1 milliseconds with a 12,000 lumen LED pulse that overexposes the targeted camera’s sensor.

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