Adversarial Artificial Intelligence: Implications for Military Operations

Abstract

This technical note (TN) describes an initial foray into understanding how physical changes to the appearance of military vehicles resulted in performance degradation for a convolutional neural network (CNN). The military vehicles chosen were the M2 Bradley Infantry Fighting Vehicle and the M1064 Mortar Carrier. As stand-ins for the actual vehicle, plastic scale models were used, each a 1/35 scale replica. The results of this research have yielded a curated training and test data set of images related to the M2 and M1064, trained models based on a combined ResNet / Inception implementation from the Keras project, and adversarial examples mocked up using the scale models with images taken by a smartphone.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2022
Accession Number
AD1173342

Entities

People

  • Harland Yu
  • Saransh Chopra

Organizations

  • United States Army Corps of Engineers

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Autonomous Vehicles
  • Classification
  • Convolutional Neural Networks
  • Data Sets
  • Deep Learning
  • Detection
  • Dimensionality Reduction
  • Graphics Processing Unit
  • Image Classification
  • Infantry Fighting Vehicles
  • Machine Learning
  • Military Vehicles
  • Neural Networks
  • Remote Sensing
  • Scale Models
  • Vehicles

Readers

  • Military Science
  • Neural Network Machine Learning.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks