# Adversarial Examples for Edge Detection: They Exist, and They Transfer

arXiv: https://arxiv.org/abs/1906.00335 · HTML: https://mlsanity.com/paper/1906.00335/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Semantically Stealthy Adversarial Attacks against Segmentation Models](https://mlsanity.com/paper/2104.01732/) (2021)
- [Shadows Aren't So Dangerous After All: A Fast and Robust Defense Against Shadow-Based Adversarial Attacks](https://mlsanity.com/paper/2208.09285/) (2022)
- [Rule-Based Spatial Mixture-of-Experts U-Net for Explainable Edge Detection](https://mlsanity.com/paper/2602.05100/) (2026)

## Abstract (arXiv)

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.