# A Survey of Fragile Model Watermarking

arXiv: https://arxiv.org/abs/2406.04809 · HTML: https://mlsanity.com/paper/2406.04809/

## Key facts

- Cited by 1 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 21 of its 59 references that are in the map.
- First posted on arXiv 7 Jun 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, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [Disentangling by Factorising](https://mlsanity.com/paper/1802.05983/) (2018)
- [Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks](https://mlsanity.com/paper/1805.12185/) (2018)
- [Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering](https://mlsanity.com/paper/1811.03728/) (2018)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search](https://mlsanity.com/paper/1903.12269/) (2019)
- [A survey of deep neural network watermarking techniques](https://mlsanity.com/paper/2103.09274/) (2021)
- [An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences](https://mlsanity.com/paper/2111.08429/) (2021)
- [An Entropy-based Pruning Method for CNN Compression](https://mlsanity.com/paper/1706.05791/) (2017)
- [RADAR: Run-time Adversarial Weight Attack Detection and Accuracy Recovery](https://mlsanity.com/paper/2101.08254/) (2021)
- [Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication](https://mlsanity.com/paper/2104.04268/) (2021)
- [Neural network fragile watermarking with no model performance degradation](https://mlsanity.com/paper/2208.07585/) (2022)
- [PublicCheck: Public Integrity Verification for Services of Run-time Deep Models](https://mlsanity.com/paper/2203.10902/) (2022)
- [DeepiSign: Invisible Fragile Watermark to Protect the Integrityand Authenticity of CNN](https://mlsanity.com/paper/2101.04319/) (2021)
- [VeriDL: Integrity Verification of Outsourced Deep Learning Services (Extended Version)](https://mlsanity.com/paper/2107.00495/) (2021)
- [Fragile Model Watermark for integrity protection: leveraging boundary volatility and sensitive sample-pairing](https://mlsanity.com/paper/2404.07572/) (2024)

## Built on by (most cited first, 1 of 1)

- [DTAMS: High-Capacity Generative Steganography via Dynamic Multi-Timestep Selection and Adaptive Deviation Mapping in Latent Diffusion](https://mlsanity.com/paper/2602.01160/) (2026)

## Abstract (arXiv)

Model fragile watermarking, inspired by both the field of adversarial attacks on neural networks and traditional multimedia fragile watermarking, has gradually emerged as a potent tool for detecting tampering, and has witnessed rapid development in recent years. Unlike robust watermarks, which are widely used for identifying model copyrights, fragile watermarks for models are designed to identify whether models have been subjected to unexpected alterations such as backdoors, poisoning, compression, among others. These alterations can pose unknown risks to model users, such as misidentifying stop signs as speed limit signs in classic autonomous driving scenarios. This paper provides an overview of the relevant work in the field of model fragile watermarking since its inception, categorizing them and revealing the developmental trajectory of the field, thus offering a comprehensive survey for future endeavors in model fragile watermarking.