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Conference papers

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.
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Conference papers
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Contributor : Laurent Amsaleg Connect in order to contact the contributor
Submitted on : Wednesday, February 3, 2021 - 9:42:02 AM
Last modification on : Wednesday, November 3, 2021 - 8:09:29 AM
Long-term archiving on: : Tuesday, May 4, 2021 - 6:22:40 PM


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  • HAL Id : hal-03129778, version 1


Zhuoran Liu, Zhengyu Zhao, Martha Larson, Laurent Amsaleg. Exploring Quality Camouflage for Social Images. MediaEval 2020 - MediaEval Benchmarking Initiative for Multimedia Evaluation, Dec 2020, Online, United States. pp.1-3. ⟨hal-03129778⟩



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