# CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection

arXiv: https://arxiv.org/abs/2008.08281 · HTML: https://mlsanity.com/paper/2008.08281/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [SoK: On the Semantic AI Security in Autonomous Driving](https://mlsanity.com/paper/2203.05314/) (2022)
- [Zero-Query Transfer Attacks on Context-Aware Object Detectors](https://mlsanity.com/paper/2203.15230/) (2022)
- [GLOW: Global Layout Aware Attacks on Object Detection](https://mlsanity.com/paper/2302.14166/) (2023)
- [On Feasibility of Intent Obfuscating Attacks](https://mlsanity.com/paper/2408.02674/) (2024)

## Abstract (arXiv)

Deep neural network based object detection hasbecome the cornerstone of many real-world applications. Alongwith this success comes concerns about its vulnerability tomalicious attacks. To gain more insight into this issue, we proposea contextual camouflage attack (CCA for short) algorithm to in-fluence the performance of object detectors. In this paper, we usean evolutionary search strategy and adversarial machine learningin interactions with a photo-realistic simulated environment tofind camouflage patterns that are effective over a huge varietyof object locations, camera poses, and lighting conditions. Theproposed camouflages are validated effective to most of the state-of-the-art object detectors.