HyperFace Camouflage

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Update April 20: HyperFace launch is being pushed back until August 2017. In the meantime, prototype garments are being printed and tested in Berlin.

HyperFace OpenCV Prototype ©Adam Harvey

HyperFace is being developed for Hyphen Labs NeuroSpeculative AfroFeminism project at Sundance Film Festival and is a collaboration with Hyphen Labs members Ashley Baccus-Clark, Carmen Aguilar y Wedge, Ece Tankal, Nitzan Bartov, and JB Rubinovitz.

NeuroSpeculative AfroFeminism is a transmedia exploration of black women and the roles they play in technology, society and culture—including speculative products, immersive experiences and neurocognitive impact research. Using fashion, cosmetics and the economy of beauty as entry points, the project illuminates issues of privacy, transparency, identity and perception.

HyperFace is a new kind of camouflage that aims to reduce the confidence score of facial detection and recognition by providing false faces that distract computer vision algorithms. HyperFace development began in 2013 and was first presented at 33c3 in Hamburg, Germany on December 30th, 2016. HyperFace will launch as a textile print at Sundance Film Festival on January 16, 2017.

Together HyperFace and NeuroSpeculative AfroFeminism will explore an Afrocentric countersurveillance aesthetic.

For more information about NeuroSpeculative AfroFeminism visit nsaf.space

How Does HyperFace Work?

HyperFace works by providing maximally activated false faces based on ideal algorithmic representations of a human face. These maximal activations are targeted for specific algorithms. The prototype above is specific to OpenCV’s default frontalface profile. Other patterns target convolutional nueral networks and HoG/SVM detectors. The technical concept is an extension of earlier work on CV Dazzle. The difference between the two projects is that HyperFace aims to alter the surrounding area (ground) while CV Dazzle targets the facial area (figure). In camouflage, the objective is often to minimize the difference between figure and ground. HyperFace reduces the confidence score of the true face (figure) by redirecting more attention to the nearby false face regions (ground).

Conceptually, HyperFace recognizes that completely concealing a face to facial detection algorithms remains a technical and aesthetic challenge. Instead of seeking computer vision anonymity through minimizing the confidence score of a true face (i.e. CV Dazzle), HyperFace offers a higher confidence score for a nearby false face by exploiting a common algorithmic preference for the highest confidence facial region. In other words, if a computer vision algorithm is expecting a face, give it what it wants.

How Well Does This Work?

There are several ways to measure the effectiveness of this method. The simplest method is to visualize the the number of false-faces detected. The parameters for scale factor, min/max size, and canny thresholding should be tuned to your specific application. Another way is to visualize the saliency map. This isn’t part of the standard OpenCV library. It can calculated by converting the 0-overlap areas into a heat map. Here the scale factor was reduced to 1.05 and the minimum size to 24. The saliency map below shows which parts of the HyperFace graphics triggered the face detection algorithm.

HyperFace saliency map with frontalface default profile ©Adam Harvey

Further testing will need to include various camera angles, illumination, distances, and alignment. The latest results will be presented at Obfuscation Conference at NYU in April.

NB: The patterns above are only prototypes and do not reflect the production designs.


Notifications

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