# MEA-Defender: A Robust Watermark against Model Extraction Attack

arXiv: https://arxiv.org/abs/2401.15239 · HTML: https://mlsanity.com/paper/2401.15239/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 32 of its 59 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring](https://mlsanity.com/paper/1802.04633/) (2018)
- [Knockoff Nets: Stealing Functionality of Black-Box Models](https://mlsanity.com/paper/1812.02766/) (2018)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Blind Backdoors in Deep Learning Models](https://mlsanity.com/paper/2005.03823/) (2020)
- [PRADA: Protecting against DNN Model Stealing Attacks](https://mlsanity.com/paper/1805.02628/) (2018)
- [Entangled Watermarks as a Defense against Model Extraction](https://mlsanity.com/paper/2002.12200/) (2020)
- [Thieves on Sesame Street! Model Extraction of BERT-based APIs](https://mlsanity.com/paper/1910.12366/) (2019)
- [DAWN: Dynamic Adversarial Watermarking of Neural Networks](https://mlsanity.com/paper/1906.00830/) (2019)
- [MAZE: Data-Free Model Stealing Attack Using Zeroth-Order Gradient Estimation](https://mlsanity.com/paper/2005.03161/) (2020)
- [SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)](https://mlsanity.com/paper/2108.04974/) (2021)
- [Very Deep Convolutional Neural Networks for Raw Waveforms](https://mlsanity.com/paper/1610.00087/) (2016)
- [How to Prove Your Model Belongs to You: A Blind-Watermark based Framework to Protect Intellectual Property of DNN](https://mlsanity.com/paper/1903.01743/) (2019)
- [Robust Watermarking of Neural Network with Exponential Weighting](https://mlsanity.com/paper/1901.06151/) (2019)
- [Defending against Model Stealing via Verifying Embedded External Features](https://mlsanity.com/paper/2112.03476/) (2021)
- [SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders](https://mlsanity.com/paper/2201.11692/) (2022)
- [Stealing Neural Networks via Timing Side Channels](https://mlsanity.com/paper/1812.11720/) (2018)
- [Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders](https://mlsanity.com/paper/2201.07513/) (2022)
- [Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack](https://mlsanity.com/paper/2105.00623/) (2021)

## Built on by (most cited first, 17 of 17)

- [ImF: Implicit Fingerprint for Large Language Models](https://mlsanity.com/paper/2503.21805/) (2025)
- [HoneypotNet: Backdoor Attacks Against Model Extraction](https://mlsanity.com/paper/2501.01090/) (2025)
- ["Yes, My LoRD." Guiding Language Model Extraction with Locality Reinforced Distillation](https://mlsanity.com/paper/2409.02718/) (2024)
- [Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks](https://mlsanity.com/paper/2504.17421/) (2025)
- [GENIE: Watermarking Graph Neural Networks for Link Prediction](https://mlsanity.com/paper/2406.04805/) (2024)
- [Revisiting Black-Box Model Ownership Verification through Information Theory](https://mlsanity.com/paper/2409.06130/) (2024)
- [THEMIS: Towards Practical Intellectual Property Protection for Post-Deployment On-Device Deep Learning Models](https://mlsanity.com/paper/2503.23748/) (2025)
- [EditMF: Drawing an Invisible Fingerprint for Your Large Language Models](https://mlsanity.com/paper/2508.08836/) (2025)
- [ChainMarks: Securing DNN Watermark with Cryptographic Chain](https://mlsanity.com/paper/2505.04977/) (2025)
- [Neural Honeytrace: Plug&Play Watermarking Framework against Model Extraction Attacks](https://mlsanity.com/paper/2501.09328/) (2025)
- [Hot-Swap MarkBoard: An Efficient Black-box Watermarking Approach for Large-scale Model Distribution](https://mlsanity.com/paper/2507.20650/) (2025)
- [Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks](https://mlsanity.com/paper/2511.07947/) (2025)
- [Server-Enforced Watermarking in U-Shaped Split Federated Learning](https://mlsanity.com/paper/2511.14422/) (2025)
- [FastFHE: Packing-Scalable and Depthwise-Separable CNN Inference Over FHE](https://mlsanity.com/paper/2511.22434/) (2025)
- [T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking](https://mlsanity.com/paper/2606.11698/) (2026)
- [Understanding Security and Privacy Perceptions of Content Creators Regarding AI Labels of AI-Generated Content](https://mlsanity.com/paper/2608.08129/) (2026)
- [Steal the Knowledge, Inherit the Mark: TwinMark for Distillation Watermarking via Second Moments and Class Multiplexing](https://mlsanity.com/paper/2609.19011/) (2026)

## Abstract (arXiv)

Recently, numerous highly-valuable Deep Neural Networks (DNNs) have been trained using deep learning algorithms. To protect the Intellectual Property (IP) of the original owners over such DNN models, backdoor-based watermarks have been extensively studied. However, most of such watermarks fail upon model extraction attack, which utilizes input samples to query the target model and obtains the corresponding outputs, thus training a substitute model using such input-output pairs. In this paper, we propose a novel watermark to protect IP of DNN models against model extraction, named MEA-Defender. In particular, we obtain the watermark by combining two samples from two source classes in the input domain and design a watermark loss function that makes the output domain of the watermark within that of the main task samples. Since both the input domain and the output domain of our watermark are indispensable parts of those of the main task samples, the watermark will be extracted into the stolen model along with the main task during model extraction. We conduct extensive experiments on four model extraction attacks, using five datasets and six models trained based on supervised learning and self-supervised learning algorithms. The experimental results demonstrate that MEA-Defender is highly robust against different model extraction attacks, and various watermark removal/detection approaches.