GENERALIZING ROBUSTNESS VERIFICATION FOR MACHINE LEARNING
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task. Although a lot of work has been done to quantify the robustness of DNN’s to ℓₚ norm bounded adversarial attacks there are still a few gaps between available guarantees and those needed in pr...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/138996 |