---
title: "Adversarial Examples in Physical World"
authors:
  - "Jiakai Wang"
doi: "10.24963/ijcai.2021/694"
url: "https://ijcai.org/proceedings/2021/694"
pdf: "https://ijcai.org/proceedings/2021/0694.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4925-4926"
track: "Doctoral Consortium"
keywords:
  - "AI Ethics, Trust, Fairness: Trustable Learning"
  - "Machine Learning: Adversarial Machine Learning"
  - "AI Ethics, Trust, Fairness: Explainability"
---

# Adversarial Examples in Physical World

Jiakai Wang

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4925-4926. DOI: [10.24963/ijcai.2021/694](https://doi.org/10.24963/ijcai.2021/694)

## Abstract

Although deep neural networks (DNNs) have already made fairly high achievements and a very wide range of impact, their vulnerability attracts lots of interest of researchers towards related studies about artificial intelligence (AI) safety and robustness this year. A series of works reveals that the current DNNs are always misled by elaborately designed adversarial examples. And unfortunately, this peculiarity also affects real-world AI applications and places them at potential risk.  we are more interested in physical attacks due to their implementability in the real world. The study of physical attacks can effectively promote the application of AI techniques, which is of great significance to the security development of AI.