Active Learning Over a Network-Based DDDAS for Anomaly Detection and Class Discovery

Abstract

Main Accomplishments/Successes: A. Novel powerful techniques were tested for confidently detecting or certifying against imperceptible backdoor data poisoning of deep neural network image classifiers. B. Novel powerful techniques were developed for confidently detecting or certifying perceptible but scene-plausible backdoor data poisoning of neural network image classifiers. C. Novel techniques were developed for confidently detecting or certifying against data poisoning attacks on training datasets and sanitizing the data, to remove the data-poisoned training samples. D. Novel techniques were developed for detecting reverse engineering attacks on classifiers.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2021
Accession Number
AD1153896

Entities

People

  • David A. B. Miller

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automata Theory
  • Change Detection
  • Computational Science
  • Computers
  • Convolutional Neural Networks
  • Deep Learning
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Information Science
  • Machine Learning
  • Network Science
  • Neural Networks
  • Pattern Recognition
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Cybersecurity.
  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks