# Deep Learning Model Inversion Attacks and Defenses: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2501.18934 · HTML: https://mlsanity.com/paper/2501.18934/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [FLARE: A Wireless Side-Channel Fingerprinting Attack on Federated Learning](https://mlsanity.com/paper/2512.10296/) (2025)
- [Privacy in Federated Learning with Spiking Neural Networks](https://mlsanity.com/paper/2511.21181/) (2025)
- [AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective](https://mlsanity.com/paper/2603.24857/) (2026)
- [Poison to Detect: Detection of Targeted Overfitting in Federated Learning](https://mlsanity.com/paper/2509.11974/) (2025)
- [Integrating RCTs, RWD, AI/ML and Statistics: Next-Generation Evidence Synthesis](https://mlsanity.com/paper/2511.19735/) (2025)
- [Show Me What You Don't Know: Efficient Sampling from Invariant Sets for Model Validation](https://mlsanity.com/paper/2603.21782/) (2026)
- [Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systems](https://mlsanity.com/paper/2606.25533/) (2026)
- [FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels](https://mlsanity.com/paper/2607.15469/) (2026)
- [Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching](https://mlsanity.com/paper/2608.16791/) (2026)

## Abstract (arXiv)

The rapid adoption of deep learning in sensitive domains has brought tremendous benefits. However, this widespread adoption has also given rise to serious vulnerabilities, particularly model inversion (MI) attacks, posing a significant threat to the privacy and integrity of personal data. The increasing prevalence of these attacks in applications such as biometrics, healthcare, and finance has created an urgent need to understand their mechanisms, impacts, and defense methods. This survey aims to fill the gap in the literature by providing a structured and in-depth review of MI attacks and defense strategies. Our contributions include a systematic taxonomy of MI attacks, extensive research on attack techniques and defense mechanisms, and a discussion about the challenges and future research directions in this evolving field. By exploring the technical and ethical implications of MI attacks, this survey aims to offer insights into the impact of AI-powered systems on privacy, security, and trust. In conjunction with this survey, we have developed a comprehensive repository to support research on MI attacks and defenses. The repository includes state-of-the-art research papers, datasets, evaluation metrics, and other resources to meet the needs of both novice and experienced researchers interested in MI attacks and defenses, as well as the broader field of AI security and privacy. The repository will be continuously maintained to ensure its relevance and utility. It is accessible at https://github.com/overgter/Deep-Learning-Model-Inversion-Attacks-and-Defenses.