Operational Feasibility of Adversarial Attacks Against Artificial Intelligence
by Li Ang Zhang, Gavin S. Hartnett, Jair Aguirre, Andrew J. Lohn, Inez Khan, Marissa Herron, Caolionn O'Connell
A large body of academic literature describes myriad attack vectors and suggests that most of the U.S. Department of Defense's (DoD's) artificial intelligence (AI) systems are in constant peril. However, RAND researchers investigated adversarial attacks designed to hide objects (causing algorithmic false negatives) and found that many attacks are operationally infeasible to design and deploy because of high knowledge requirements and impractical attack vectors. As the researchers discuss in this report, there are tried-and-true nonadversarial techniques that can be less expensive, more practical, and often more effective. Thus, adversarial attacks against AI pose less risk to DoD applications than academic research currently implies. Nevertheless, well-designed AI systems, as well as mitigation strategies, can further weaken the risks of such attacks.
Deception Detection
by Marek N. Posard, Christian Johnson, Julia L. Melin, Emily Ellinger, Hilary Reininger
A group of RAND Corporation researchers found that machine-learning (ML) models can identify signs of deception during national security background check interviews. The most accurate approach for detecting deception is an ML model that counts the number of times that interviewees use common words.
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OCEANS 17 Tabletop
Exercise
Driving to Safety
How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability?
Nidhi Kalra, Susan M. Paddock
A Review of Selected International Aircraft Spares Pooling Programs
Lessons Learned for F-35 Spares Pooling
by Mark A. Lorell, James Pita
Enabling Early Sustainment Decisions
Application to F-35 Depot-Level Maintenance
by John G. Drew, Ronald G. McGarvey, Peter Buryk
Dire Strait?
Military Aspects of the China-Taiwan Confrontation and Options for U.S. Policy
by David A. Shlapak, David T. Orletsky, Barry Wilson