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- W3092611067 abstract "The resizing of images, which is typically a required part of preprocessing for computer vision systems, is vulnerable to attack. Images can be created such that the image is completely different at machine-vision scales than at other scales and the default settings for some common computer vision and machine learning systems are vulnerable. We show that defenses exist and are trivial to administer provided that defenders are aware of the threat. These attacks and defenses help to establish the role of input sanitization in machine learning." @default.
- W3092611067 created "2020-10-15" @default.
- W3092611067 creator A5052029742 @default.
- W3092611067 date "2020-10-06" @default.
- W3092611067 modified "2023-09-27" @default.
- W3092611067 title "Downscaling Attack and Defense: Turning What You See Back Into What You Get." @default.
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