Our Collective Noise (OCN): A tactical response to computer vision, surveillance, and noise
Okudan, Alaz
Okudan, Alaz
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Publication Date
2025-07-09
Type
conference paper
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Citation
Okudan, Alaz. (2025). Our Collective Noise (OCN): A tactical response to computer vision, surveillance, and noise. Paper presented at the XCOAX 2025, 13th Conference on Computation, Communication, Aesthetics & X. Dundee, Scotland, 9-11 July, https://doi.org/10.34626/2025_xcoax_x_02
Abstract
Our Collective Noise (OCN) is a research-based tactical media project. As an attempt to transform the top-down pervasive qualities of machine learning (ML), computer vision (CV), and surveillance technologies into a bottom-up tactical tool, it plays around the concepts of noise, de-identification, accidental aesthetics, and human-machine collaboration. OCN, as an offline system, uses live webcam feed, ML, and CV to detect people and simultaneously turn them into coarse pixels to replace the common aim of precise identification in surveillance technologies with anonymity. Coarse pixels are constantly stitched together to create collective abstract human-machine interaction patterns that people are collectively and unidentifiably part of. In a world where thriving ML, CV, and AI (artificial intelligence) technologies increasingly rely on cleaner datasets, higher processing capacities, precise labels and categories, OCN turns the technology against itself, in pursuit of revealing the latent potential in noise, anonymity, and collective action.
Publisher
school of x in connection with xCoAx class of 2025
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CC BY-NC-SA