# GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detection

arXiv: https://arxiv.org/abs/2107.09130 · HTML: https://mlsanity.com/paper/2107.09130/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree](https://mlsanity.com/paper/2002.08653/) (2020)
- [Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection](https://mlsanity.com/paper/1708.06525/) (2017)
- [Raise Your Game for Split Manufacturing: Restoring the True Functionality Through BEOL](https://mlsanity.com/paper/1806.09135/) (2018)

## Built on by (most cited first, 14 of 14)

- [CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation](https://mlsanity.com/paper/2404.08806/) (2024)
- [HW2VEC: A Graph Learning Tool for Automating Hardware Security](https://mlsanity.com/paper/2107.12328/) (2021)
- [NetDeTox: Adversarial and Efficient Evasion of Hardware-Security GNNs via RL-LLM Orchestration](https://mlsanity.com/paper/2512.00119/) (2025)
- [LLMPirate: LLMs for Black-box Hardware IP Piracy](https://mlsanity.com/paper/2411.16111/) (2024)
- [AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning](https://mlsanity.com/paper/2402.13946/) (2024)
- [Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs](https://mlsanity.com/paper/2211.16495/) (2022)
- [Device-Bind Key-Storageless Hardware AI Model IP Protection: A PUF and Permute-Diffusion Encryption-Enabled Approach](https://mlsanity.com/paper/2212.11133/) (2022)
- [Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses](https://mlsanity.com/paper/2508.19641/) (2025)
- [Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network](https://mlsanity.com/paper/2207.06664/) (2022)
- [Free and Fair Hardware: A Pathway to Copyright Infringement-Free Verilog Generation using LLMs](https://mlsanity.com/paper/2505.06096/) (2025)
- [A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation](https://mlsanity.com/paper/2204.09579/) (2022)
- [Circuit Diagram Retrieval Based on Hierarchical Circuit Graph Representation](https://mlsanity.com/paper/2503.11658/) (2025)
- [Wrong Code, Right Structure: Learning Netlist Representations from Imperfect LLM-Generated RTL](https://mlsanity.com/paper/2603.09161/) (2026)
- [ReDIL-GNN: Resynthesis Domain Incremental Learning for Circuit Graph Neural Networks](https://mlsanity.com/paper/2609.18595/) (2026)

## Abstract (arXiv)

Aggressive time-to-market constraints and enormous hardware design and fabrication costs have pushed the semiconductor industry toward hardware Intellectual Properties (IP) core design. However, the globalization of the integrated circuits (IC) supply chain exposes IP providers to theft and illegal redistribution of IPs. Watermarking and fingerprinting are proposed to detect IP piracy. Nevertheless, they come with additional hardware overhead and cannot guarantee IP security as advanced attacks are reported to remove the watermark, forge, or bypass it. In this work, we propose a novel methodology, GNN4IP, to assess similarities between circuits and detect IP piracy. We model the hardware design as a graph and construct a graph neural network model to learn its behavior using the comprehensive dataset of register transfer level codes and gate-level netlists that we have gathered. GNN4IP detects IP piracy with 96% accuracy in our dataset and recognizes the original IP in its obfuscated version with 100% accuracy.