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Pixel Currency

Current smart device platforms increasingly encourage instantaneous cloud based image syncing and sharing. There is certain nonchalance towards the idea of intimate memories residing in remote cloud servers. Meanwhile, Facebook’s new deep face learning algorithm (accurate to 97.7%) is closing the gap to human level performance in identity verification.

The increased societal acceptance towards cloud storage coupled with the recent advances in computer vision technology, affords a vision for a solution for arising identity privacy concerns in public spaces.

‘Pixel Currency‘ addresses the issue of identity privacy arising from the rise of public imaging devices in the near-future. It proposes the implementation of a central screening mechanism for all captured image data. This mechanism, governed by rules defined at the state level, would operate at two levels:

That of the individual; Allowing a citizen to define the settings for the viewership rights of his captured form. They may choose to commoditize their form or protect it.

That of the Volumetric Estate owners; Allowing entities to own set XYZ co-ordinates, traded according to value and popularity. The price of image capturing these ‘Volumes’ is dictated by the underlying intent of the image capturer.

The individual settings of a citizen, coupled with the tariff model of the ‘Volume’ would dictate the materialization and the price of the digital image. Images might be free, free (sponsored), with limited licensing, full licensing or silenced.

The project hints at possible ways of commodification of identities and spaces when an image screening mechanism is implemented at the network level. Thus aims to show both the positive and negative implications of a technology, which is created with the intent of addressing the issue of identity privacy in the ubiquitous imaging paradigm.

A one minute video showcases the image capturing experience with both identity and space commodification.
A photo manipulation / viewing app is exhibited with the video, where viewers can engage with few sample images and explore various tariffs models.

Thanks:

Anthony Dunne, Debbie Ding, Ira Pappa, James Auger, Keith Kenniff, Manikandan HK, Michela Tiberio, Nitasha Jhala, Promila Roychoudhury, Rachel Knoll, Rodrigo Ledbrun, Sam Conran, Shruti Grover

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