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Oral
Synthesizing Robust Adversarial Examples
Anish Athalye · Logan Engstrom · Andrew Ilyas · Kevin Kwok

Wed Jul 11 08:40 AM -- 08:50 AM (PDT) @ A7
in Deep Learning (Adversarial) 1 »

Standard methods for generating adversarial examples for neural networks do not consistently fool neural network classifiers in the physical world due to a combination of viewpoint shifts, camera noise, and other natural transformations, limiting their relevance to real-world systems. We demonstrate the existence of robust 3D adversarial objects, and we present the first algorithm for synthesizing examples that are adversarial over a chosen distribution of transformations. We synthesize two-dimensional adversarial images that are robust to noise, distortion, and affine transformation. We apply our algorithm to complex three-dimensional objects, using 3D-printing to manufacture the first physical adversarial objects. Our results demonstrate the existence of 3D adversarial objects in the physical world.

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Author Information

Anish Athalye (MIT CSAIL)
Logan Engstrom (MIT)
Andrew Ilyas (Massachusetts Institute of Technology)
Kevin Kwok (LabSix)

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