HyperFace

False-Face Camouflage

Posted 2017-03-01 in prototypes tagged #computer-vision #face-detection #camouflage #fashion #counter-surveillance
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HyperFace Prototype. Rendering by Ece Tankal / hyphen-labs.com. 2017
HyperFace Prototype. Rendering by Ece Tankal / hyphen-labs.com. 2017

False-face computer vision camouflage patterns for Hyphen Labs’ NeuroSpeculative AfroFeminism at Sundance Film Festival 2017.

Collaboration

The HyperFace (Version 1) prototype was developed for Hyphen-Labs NeuroSpeculative AfroFeminism project and debuted at the Sundance Film Festival in 2017. The project 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 launched as a textile print at Sundance Film Festival on January 16, 2017.

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

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 can target convolutional neural networks or HoG/SVM-based edge detectors.

The technical concept is an extension of earlier work on CV Dazzle. However, 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 (i.e. use largest face). In other words, if a computer vision algorithm is expecting a face, exploit its expectations.

How Well Does This Work?

The simplest method is to visualize the number of false-faces detected. The parameters for scale factor, min/max size, and canny thresholding should be tuned to your specific application domain.

The comparison below shows the heat-map (or saliency map) for all possible detections (0-overlap scores). In this result, the scale factor was reduced to 1.05 and the minimum size to 24 (the absolute minimum) since the scarf includes small false-faces.

The heat map is a quick, but incomplete, way to visualize whether the design is producing any false-faces, where more red is higher performance.

HyperFace saliency map. Scarf rendering by Ece Tankal / hyphen-labs.com. 2017
HyperFace saliency map. Scarf rendering by Ece Tankal / hyphen-labs.com. 2017

Notes