Abstract
Most works on adversarial examples for deeplearning based image classifiers use noise that, while small, covers the entire image. We explore the case where the noise is allowed to be visible but confined to a small, localized patch of the image, without covering any of the main object(s) in the image. We show that it is possible to generate localized adversarial noises that cover only 2% of the pixels in the image, none of them over the main object, and that are transferable across images and locations, and successfully fool a stateof-the-art Inception v3 model with very high success rates.
| Original language | English |
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| Title of host publication | 35th International Conference on Machine Learning, ICML 2018 |
| Editors | Jennifer Dy, Andreas Krause |
| Publisher | International Machine Learning Society (IMLS) |
| Pages | 3903-3911 |
| Number of pages | 9 |
| ISBN (Electronic) | 9781510867963 |
| State | Published - 2018 |
| Event | 35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden Duration: 10 Jul 2018 → 15 Jul 2018 |
Publication series
| Name | 35th International Conference on Machine Learning, ICML 2018 |
|---|---|
| Volume | 6 |
Conference
| Conference | 35th International Conference on Machine Learning, ICML 2018 |
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| Country/Territory | Sweden |
| City | Stockholm |
| Period | 10/07/18 → 15/07/18 |
Bibliographical note
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