Synthesizing Robust Adversarial Examples

Understand

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.