# EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection

arXiv: https://arxiv.org/abs/2312.12049 · HTML: https://mlsanity.com/paper/2312.12049/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Adversarial Frontier Stitching for Remote Neural Network Watermarking](https://mlsanity.com/paper/1711.01894/) (2017)
- [Deep Neural Network Fingerprinting by Conferrable Adversarial Examples](https://mlsanity.com/paper/1912.00888/) (2019)
- [Model Watermarking for Image Processing Networks](https://mlsanity.com/paper/2002.11088/) (2020)
- [Passport-aware Normalization for Deep Model Protection](https://mlsanity.com/paper/2010.15824/) (2020)
- [Training DNN Model with Secret Key for Model Protection](https://mlsanity.com/paper/2008.02450/) (2020)
- [Performance Comparison of Contemporary DNN Watermarking Techniques](https://mlsanity.com/paper/1811.03713/) (2018)

## Built on by (most cited first, 3 of 3)

- [Staining and locking computer vision models without retraining](https://mlsanity.com/paper/2507.22000/) (2025)
- [SpanKey: Dynamic Key Space Conditioning for Neural Network Access Control](https://mlsanity.com/paper/2604.12254/) (2026)
- [GoodDiffusion: Proactive Copyright Protection for Diffusion Bridge Models via Learnable Sample-specific Signatures](https://mlsanity.com/paper/2606.29759/) (2026)

## Abstract (arXiv)

In the rapidly growing digital economy, protecting intellectual property (IP) associated with digital products has become increasingly important. Within this context, machine learning (ML) models, being highly valuable digital assets, have gained significant attention for IP protection. This paper introduces a practical encryption-based framework called textitEncryIP, which seamlessly integrates a public-key encryption scheme into the model learning process. This approach enables the protected model to generate randomized and confused labels, ensuring that only individuals with accurate secret keys, signifying authorized users, can decrypt and reveal authentic labels. Importantly, the proposed framework not only facilitates the protected model to multiple authorized users without requiring repetitive training of the original ML model with IP protection methods but also maintains the model's performance without compromising its accuracy. Compared to existing methods like watermark-based, trigger-based, and passport-based approaches, textitEncryIP demonstrates superior effectiveness in both training protected models and efficiently detecting the unauthorized spread of ML models.