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Attacks, Defenses and Testing for Deep Learning

This book provides a systematic study on the security of deep learning. With its powerful learning ability, deep learning is widely used in CV, FL, GNN, RL, and other scenarios. However, during the process of application, researchers have revealed that deep learning is vulnerable to malicious attack...

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Bibliografski detalji
Glavni autori: Chen, Jinyin (Autor), Zhang, Ximin (Autor), Zheng, Haibin (Autor)
Autor kompanije: SpringerLink (Online service)
Format: Elektronički e-knjiga
Jezik:engleski
Izdano: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Izdanje:1st ed. 2024.
Teme:
Artificial intelligence.
Computer engineering.
Computer networks .
Neural networks (Computer science) .
Artificial Intelligence.
Computer Engineering and Networks.
Mathematical Models of Cognitive Processes and Neural Networks.
Online pristup:https://doi.org/10.1007/978-981-97-0425-5
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Sadržaj:
  • Perturbation Optimized Black-Box Adversarial Attacks via Genetic Algorithm
  • Feature Transfer Based Stealthy Poisoning Attack for DNNs
  • Adversarial Attacks on GNN Based Vertical Federated Learning
  • A Novel DNN Object Contour Attack on Image Recognition
  • Query-Efficient Adversarial Attack Against Vertical Federated Graph Learning
  • Targeted Label Adversarial Attack on Graph Embedding
  • Backdoor Attack on Dynamic Link Prediction
  • Attention Mechanism based Adversarial Attack against DRL
  • Characterizing Adversarial Examples via Local Gradient Checking
  • A Novel Adversarial Defense by Refocusing on Critical Areas
  • Neuron-level Inverse Perturbation Against Adversarial Attacks
  • Adaptive Channel Transformation-based Detector for Adversarial Attacks
  • Defense Against Free-rider Attack From the Weight Evolving Frequency
  • An Effective Model Copyright Protection for Federated Learning
  • Guard the vertical federated graph learning from Property Inference Attack
  • Using Adversarial Examples to Against Backdoor Attack in FL
  • Evaluating the Adversarial Robustness of Deep Model by Decision Boundaries
  • Certifiable Prioritization for Deep Neural Networks via Movement Cost in Feature Space
  • Interpretable White-Box Fairness Testing through Biased Neuron Identification
  • A Deep Learning Framework for Dynamic Network Link Prediction. .

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