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National Science Foundation

Cyber-Physical Systems Virtual Organization

Read-only archive of site from September 29, 2023.

CPS-VO

Imperceptible Attack Noise

biblio

Visible to the public TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks

Submitted by grigby1 on Wed, 11/04/2020 - 2:15pm
  • pubcrawl
  • learning (artificial intelligence)
  • machine learning
  • ML Security
  • multilevel security system
  • object detection
  • Object recognition
  • optimization
  • Optimization algorithms
  • perceptible noise
  • pre-trained DNNs
  • Inference algorithms
  • resilience
  • Resiliency
  • Scalability
  • security
  • security of data
  • structural similarity analysis
  • traffic sign detection
  • Training
  • training data-unaware imperceptible security attacks
  • training dataset
  • feature extraction
  • AI Poisoning
  • automation
  • autonomous vehicles
  • convolutional neural nets
  • Correlation
  • data manipulation attacks
  • data poisoning attacks
  • Deep Neural Network
  • deep neural networks
  • DNNs
  • Adversarial Machine Learning
  • generated attack images
  • German Traffic Sign Recognition Benchmarks dataset
  • Human behavior
  • image classification
  • Image coding
  • image recognition
  • imperceptibility factor
  • imperceptible attack images
  • Imperceptible Attack Noise

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