Adversarial Machine Learning
GPTKB entity
Statements (47)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Machine Learning
|
| gptkbp:hasCitations |
Data Poisoning
Interpretability Model Robustness Transferability Evasion Attacks |
| gptkbp:hasImpactOn |
Regulatory Compliance
Model Performance AI Safety User Privacy Trust in AI |
| gptkbp:hasTechnology |
Adversarial Training
Adversarial Examples Defensive Distillation Gradient Masking Feature Squeezing |
| https://www.w3.org/2000/01/rdf-schema#label |
Adversarial Machine Learning
|
| gptkbp:isAvenueFor |
gptkb:Autonomous_Vehicles
Fraud Detection Speech Recognition Spam Detection Facial_Recognition |
| gptkbp:isChallengedBy |
Adversarial Attacks
Scalability Issues Lack of Standardization Model Overfitting Data Integrity Issues |
| gptkbp:isExploredIn |
Policy Making
Industry Applications Public Awareness Ethical Discussions Academic_Research |
| gptkbp:isRelatedTo |
Deep Learning
Ethics Neural Networks Security Robustness |
| gptkbp:isSupportedBy |
Conferences
Online Courses Research Papers Workshops Open Source Tools |
| gptkbp:isUsedIn |
Cybersecurity
Natural Language Processing Robotics Computer_Vision |
| gptkbp:isVisitedBy |
Researchers
|