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

Source: [CCA: Exploring the Possibility of Contextual Camouflage Attack on Object Detection](<https://www.sota2.com/research/paper/68fa682a1e209131cea1a70a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:30:00.158Z

Authors: Shengnan Hu, Yang Zhang, Sumit Laha, Ankit Sharma, Hassan Foroosh

Published: 2020-08-19T06:16:10.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2008.08281>)

[PDF](<https://arxiv.org/pdf/2008.08281.pdf>)