  Convolutional neural networks have recently advanced the state of the art in
many tasks including edge and object boundary detection. However, in this
paper, we demonstrate that these edge detectors inherit a troubling property of
neural networks: they can be fooled by adversarial examples. We show that
adding small perturbations to an image causes HED, a CNN-based edge detection
model, to fail to locate edges, to detect nonexistent edges, and even to
hallucinate arbitrary configurations of edges. More surprisingly, we find that
these adversarial examples transfer to other CNN-based vision models. In
particular, attacks on edge detection result in significant drops in accuracy
in models trained to perform unrelated, high-level tasks like image
classification and semantic segmentation. Our code will be made public.
