# Intellectual Property Protection for Deep Learning Model and Dataset Intelligence

arXiv: https://arxiv.org/abs/2411.05051 · HTML: https://mlsanity.com/paper/2411.05051/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2024.

## Built on by (most cited first, 6 of 6)

- [DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation](https://mlsanity.com/paper/2505.19504/) (2025)
- [Toward Robust Non-Transferable Learning: A Survey and Benchmark](https://mlsanity.com/paper/2502.13593/) (2025)
- [Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models](https://mlsanity.com/paper/2505.02824/) (2025)
- [MarkTune: Improving the Quality-Detectability Trade-off in Open-Weight LLM Watermarking](https://mlsanity.com/paper/2512.04044/) (2025)
- [ADS-C: Antidistillation Sampling for Classification](https://mlsanity.com/paper/2607.15467/) (2026)
- [Stemma: Induced Decision Regions Reveal LLM Provenance](https://mlsanity.com/paper/2607.25880/) (2026)

## Abstract (arXiv)

With the growing applications of Deep Learning (DL), especially recent spectacular achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the commercial significance of these remarkable models has soared. However, acquiring well-trained models is costly and resource-intensive. It requires a considerable high-quality dataset, substantial investment in dedicated architecture design, expensive computational resources, and efforts to develop technical expertise. Consequently, safeguarding the Intellectual Property (IP) of well-trained models is attracting increasing attention. In contrast to existing surveys overwhelmingly focusing on model IPP mainly, this survey not only encompasses the protection on model level intelligence but also valuable dataset intelligence. Firstly, according to the requirements for effective IPP design, this work systematically summarizes the general and scheme-specific performance evaluation metrics. Secondly, from proactive IP infringement prevention and reactive IP ownership verification perspectives, it comprehensively investigates and analyzes the existing IPP methods for both dataset and model intelligence. Additionally, from the standpoint of training settings, it delves into the unique challenges that distributed settings pose to IPP compared to centralized settings. Furthermore, this work examines various attacks faced by deep IPP techniques. Finally, we outline prospects for promising future directions that may act as a guide for innovative research.