Exploring Quality Camouflage for Social Images
Abstract
Social images can be misused in ways not anticipated or intended by the people who share them online. In particular, high-quality images can be driven to unwanted prominence by search engines or used to train unscrupulous AI. The risk of misuse can be reduced if photos can evade quality filtering, which is commonly carried out by automatic Blind Image Quality Assessment (BIQA) algorithms. The Pixel Privacy Task benchmarks privacy-protective approaches that shield images against unethical computer vision algorithms. In the 2020 task, participants are asked to develop quality camouflage methods that can effectively decrease the BIQA score of high-quality images while maintaining image appeal. The camouflage should not damage the image from the point of view of the user: it needs to be either imperceptible, or else to enhance the image visibly, to the human eye.
Domains
Computer Science [cs]Origin | Files produced by the author(s) |
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