Establishing security and trust for object detection and classification with confidential AI

Richard Searle; Prabhanjan Gururaj

doi:10.1117/12.2618303

6 June 2022 Establishing security and trust for object detection and classification with confidential AI

Richard Searle, Prabhanjan Gururaj

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130C (2022) https://doi.org/10.1117/12.2618303
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

In the context of multi-domain operations (MDO), artificial intelligence (AI) systems support human operators by processing large volumes of electro-optical/infrared (EOIR) sensor data. In this paper, we demonstrate how confidential computing technology, incorporating a hardware-based root of trust, can provide systemic identity verification through mutual attestation, secure the integrity of AI models, and preserve the confidentiality of processed sensor data. Using the example of aircraft detection and classification, we describe how confidential computing can defend against adversarial machine learning (AML) attacks by providing intrinsic security at the tactical edge and within the distributed applications environment that characterizes MDO.

Conference Presentation

Citation Download Citation

Richard Searle and Prabhanjan Gururaj "Establishing security and trust for object detection and classification with confidential AI", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130C (6 June 2022); https://doi.org/10.1117/12.2618303

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