Dr. Adam Cobb Profile

Dr. Adam Cobb

at SRI International

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 | 7 June 2024 Presentation + Paper

Bayesian graph representation learning for adversarial patch detection

Alexander Berenbeim, Alexander Wei, Adam Cobb, Anirban Roy, Susmit Jha, Nathaniel Bastian

Proceedings Volume 13054, 1305409 (2024) https://doi.org/10.1117/12.3013128

KEYWORDS: Object detection, Data modeling, Neural networks, Machine learning, Convolution

Read Abstract +

Representing context, reasoning within contexts, and providing quantitative assessments of machine learning (ML) model certainty are all tasks of fundamental importance for secure, interpretable, and reliable model development. Recent enthusiasm regarding generative ML models has highlighted the importance of representing context, which is contingent on relevant and contextual features of data and model predictions are unreliable on out-of-context inputs. Herein, we develop the theory of graph representation learning (GRL) to extend to Bayesian Graph Neural Networks and to incorporate various forms of uncertainty quantification to improve model development and application in the presence of adversarial attacks. Within this framework, we approach the challenge of adversarial patch detection using a synthesized dataset consisting of images from the APRICOT and COCO datasets to study various binary classification models for patch detection. We present GRL models with two layers of edge convolution that are capable of detecting patches with up to 93.5% accuracy. Further, we find evidence supporting the use of the certainty and competence framework for model predictions as a tool for detecting patches, particularly when the former is included as a model feature in graph neural networks.

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