Leveraging linear mapping for model-agnostic adversarial defense
Huma Jamil,
Yajing Liu,
Nathaniel Blanchard
et al.
Abstract:In the ever-evolving landscape of deep learning, novel designs of neural network architectures have been thought to drive progress by enhancing embedded representations. However, recent findings reveal that the embedded representations of various state-of-the-art models are mappable to one another via a simple linear map, thus challenging the notion that architectural variations are meaningfully distinctive. While these linear maps have been established for traditional non-adversarial datasets, e.g., ImageNet,… Show more
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