Supplement to Trevor Paglen “Invisible Images (Your Pictures Are Looking At You)” Reading

Trevor Paglen: http://www.paglen.com/


Martha Rosler, Semiotics of the Kitchen (1975)

 


Emory Douglas  

Emory Douglas was the Revolutionary Artist of the Black Panther Party and subsequently became its Minister of Culture, part of the national leadership. He created the overall design of the Black Panther, the Party’s weekly newspaper, and oversaw its layout and production until the Black Panthers disbanded in 1979–80. Throughout the ’60s and ’70s, Douglas made countless artworks, illustrations, and cartoons, which were reproduced in the paper and distributed as prints, posters, cards, and even sculptures. All of them utilized a straightforward graphic style and a vocabulary of images that would become synonymous with the Party and the issues it fought for. The images below are images used in these publications:

sourced from: http://africanah.org/emory-douglas-black-panther-prints/

03-09-69-B-AK.tif

 


Catherine Opie 

Catherine Opie,  Self-Portrait Cutting (detail), 1993

Catherine Opie, Chicken, from the Being and Having Series, 1992

Catherine Opie, Self-portrait / Pervert, 1994

Catherine Opie, Self-portrait / Nursing, 1994

sourced from: https://www.newyorker.com/magazine/2017/03/13/catherine-opie-all-american-subversive


Adam Harvey CV Dazzle (2010-present)

CV Dazzle is a type of camouflage from computer vision. It uses bold patterning to break apart the expected features targeted by computer vision algorithms.CV Dazzle works by altering the expected dark and light areas of a face (or object) according to the vulnerabilities of a specific computer vision algorithm. In the image above (Look #1), the design targets the Viola-Jones face detection algorithm, a popular and open source face detector that is included with the OpenCV computer vision framework. CV Dazzle designs can be created using only hair styling, makeup, and fashion accessories for any type of face.

 

Style Tips

  1. Makeup Avoid enhancers. They amplify key facial features. This makes your face easier to detect. Instead apply makeup that contrasts with your skin tone in unusual tones and directions: light colors on dark skin, dark colors on light skin.
  2. Nose Bridge Partially obscure the nose-bridge area. The region where the nose, eyes, and forehead intersect is a key facial feature. This is especially effective against OpenCV’s face detection algorithm.
  3. Eyes Partially obscure one or both of the ocular regions. The symmetrical position and darkness of eyes is a key facial feature.
  4. Masks Avoid wearing masks as they are illegal in some cities. Instead of concealing your face, modify the contrast, tonal gradients, and spatial relationship of dark and light areas using hair, makeup, and/or unique fashion accessories.
  5. Head Research from Ranran Feng and Balakrishnan Prabhakaran at University of Texas, shows that obscuring the elliptical shape of a head can also improve your ability to block face detection. Link: Facilitating fashion camouflage art. Use hair, turtlenecks, or fashion accessories to alter the expected elliptical shape.
  6. Asymmetry Face detection algorithms expect symmetry between the left and right sides of the face. By developing an asymmetrical look, you can decrease your probability of being detected.

sourced from: https://ahprojects.com/


Julian Oliver, HARVEST (2017)

HARVEST is a work of critical engineering and computational climate art. It uses wind-energy to mine cryptocurrency, the earnings of which are used as a source of funding for climate-change research. Taking the form of a 2m wind turbine with environmental sensors, weatherproof computer and 4G uplink, HARVEST ‘feeds’ from two primary symptoms of our changing climate: wind gusts and storms. It does this by transforming wind energy into the electricity required to meet the demanding task of mining cryptocurrency (here Zcash), a decentralised process where computers are financially rewarded for their work maintaining and verifying a public transaction ledger known as the blockchain. Rather than filling the digital wallet of the artist, all rewards earned by the HARVEST mining machine are paid out as donations to non-profit climate change research organisations such that they can better study this planetary-scale challenge.

Acting as a fully functional prototype beyond a media-art context, it is envisaged hundreds of such HARVEST nodes could be deployed in the windiest parts of the world, together generating large sums of supplementary funding for climate-change NGOs in a time where climate science itself is under siege from the fossil-fuelled interests of governments and corporations.

sourced from: https://julianoliver.com/output/category/projects