Abstract
This article critically examines computer vision–based pornography filtering (CVPF), a subfield in computer science seeking to train computers on how to recognize the difference between digital pornographic images and nonpornographic images. Based on a review of 102 peer-reviewed CVPF articles, we argue that CVPF has as a whole trained computers to “see” a very specific, idealized form of pornography: pictures of lone, thin, naked women. The article supports this argument by closely reading the algorithms proposed in the CVPF literature and quantitatively analyzing the images included as illustrations of these algorithms. Drawing on pornography studies, we also compare the CVPF pornographic imagination with “noisy” pornography that exceeds computer vision. Ultimately, the article argues that this very narrow imagination of porn in CVPF reflects and reinforces larger gender and sexual inequalities in the technology industry as a whole.
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