Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses
cs.CV
Submitted: 2024-02-06
Updated: 2026-09-18
Code: https://github.com/ffhibnese/Model-Inversion-Attack-ToolBox
Terminology
Sources
- NIPS 2016 Tutorial: Generative Adversarial Networks
- Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model
- Language Models are Few-Shot Learners
- Scalable Extraction of Training Data from (Production) Language Models
- Defending Model Inversion and Membership Inference Attacks via Prediction Purification
- Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment
- Extracting Training Data from Unconditional Diffusion Models
- Language Model Inversion
- Privacy Leakage in Text Classification: A Data Extraction Approach
- Are Large Pre-Trained Language Models Leaking Your Personal Information?
- Deconstructing Classifiers: Towards A Data Reconstruction Attack Against Text Classification Models
- Functional Mechanism: Regression Analysis under Differential Privacy
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
- Classifier-Free Diffusion Guidance
- Variational Approaches for Auto-Encoding Generative Adversarial Networks
- On Memorization in Diffusion Models
- Extracting Prompts by Inverting LLM Outputs
- Bag of Tricks for Training Data Extraction from Language Models
- A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
- Deep Reinforcement Learning: An Overview
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