Adversarial learning using synthetic IR imagery

James Uplinger; Derek Schesser; Christopher Meyer; Joseph Conroy; Celso de Melo

doi:10.1117/12.2663612

13 June 2023 Adversarial learning using synthetic IR imagery

James Uplinger, Derek Schesser, Christopher Meyer, Joseph Conroy, Celso de Melo

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 125290C (2023) https://doi.org/10.1117/12.2663612
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Recent years have seen impressive progress in Automatic Target Recognition (ATR) technology, both in the visible and non-visible spectra, which introduces an important challenge to the Army: understanding gaps in ATR algorithms’ feature space for informed design methodology. To tackle this challenge, we look at a combination of synthetic data and adversarial learning techniques to explore the feature space of Machine Learning (ML) algorithms. Adversarial learning, however, requires large amounts of training data representing diversity in terms of target pose, lighting, and environmental conditions. Often the main bottleneck is collecting and labeling this real training data. The problem is exacerbated in infrared (IR) given unique challenges due to material and thermal variation. Here, we present a solution based on a simulator that supports generation of physically accurate custom synthetic IR training data; this data is then leveraged to systematically study weaknesses in a state-of-the-art ATR algorithm that is often used in practice, YOLOv5. We will present results showing that this approach can lead to critical insight on algorithm weaknesses with practical consequence for the design of defense mechanisms against ATR technology as well as improved training of ML algorithms to reduce feature space vulnerabilities.

Conference Presentation

Citation Download Citation

James Uplinger, Derek Schesser, Christopher Meyer, Joseph Conroy, and Celso de Melo "Adversarial learning using synthetic IR imagery", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 125290C (13 June 2023); https://doi.org/10.1117/12.2663612

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