Negative, Suppression is Void: AI, Deception, and Fighting Machines

Abstract

This paper examines the implications for the introduction of AI and autonomous weapons systems to the battlefield. An overview of the United States current efforts in AI is compared to those of Russia and China. This paper then explains how machine learning works in order to examine how it can be deceived. This paper then examines the current state of research into deceiving AI via a literature review, focusing on adversarial examples and data poisoning attacks. This paper then postulates a threat model on how an adversary could leverage either type of attack in order to develop a method of comparing the relative strength of the two. These attacks are then conducted against simulated data.

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Document Details

Document Type
Technical Report
Publication Date
May 07, 2021
Accession Number
AD1178260

Entities

People

  • Brian J. Strom

Organizations

  • Marine Corps University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computational Science
  • Computer Languages
  • Computers
  • Data Science
  • Deep Learning
  • Information Processing
  • Information Science
  • Information Systems
  • Intrusion Detectors
  • Machine Learning
  • Military Science
  • National Security
  • Network Science
  • Neural Networks
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Military History / Militaries and War Studies
  • Neural Network Machine Learning.
  • Strategic Security Studies

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - UAVs