Mr. Jorik T. Venema Profile

Jorik T. Venema

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 13 November 2024 Presentation + Paper

Anti-AI camouflage

Alexander van Oers, Jorik Venema

Proceedings Volume 13206, 132060W (2024) https://doi.org/10.1117/12.3031144

KEYWORDS: Object detection, Camouflage, Artificial intelligence, Printing, Neural networks, Adversarial training

Read Abstract +

Following the current Russian invasion of Ukraine, the use of AI-enabled technologies is the next logical step in the use of autonomous systems (such as UAVs or loitering munitions) for detecting military assets. The survivability of military assets on the battlefield is increased by Camouflage, Concealment & Deception (CCD) measures. However, current CCD measures are inadequate to prevent detection by AI-enabled technologies. To improve on CCD measures, adversarial patterns can be employed to fool AI for object detection: Assets, such as soldiers, command tents, and vehicles, camouflaged with adversarial patterns are either not detected or are misclassified by AI. In an operational setting, the downside of adversarial patterns is that they are colorful and distinct from their surroundings. This makes them easily detectable by the human eye. In this manuscript, we design anti-AI camouflage that only use colors close to camouflage netting, as commonly used by NATO forces. We show these are effective at (a) either preventing detection, (b) reducing the confidence the AI has in its detection (c) or making the AI detect many false objects with low confidence. This anti-AI camouflage can fool both human intelligence and artificial intelligence.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years