# DRAWNAPART: A Device Identification Technique based on Remote GPU Fingerprinting

arXiv: https://arxiv.org/abs/2201.09956 · HTML: https://mlsanity.com/paper/2201.09956/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2022.

## Built on by (most cited first, 7 of 7)

- [Locking Machine Learning Models into Hardware](https://mlsanity.com/paper/2405.20990/) (2024)
- [AGFT: An Adaptive GPU Frequency Tuner for Real-Time LLM Inference Optimization](https://mlsanity.com/paper/2508.01744/) (2025)
- [Adversarial attacks and defenses on ML- and hardware-based IoT device fingerprinting and identification](https://mlsanity.com/paper/2212.14677/) (2022)
- [Targeted and Troublesome: Tracking and Advertising on Children's Websites](https://mlsanity.com/paper/2308.04887/) (2023)
- [Fingerprinting and Tracing Shadows: The Development and Impact of Browser Fingerprinting on Digital Privacy](https://mlsanity.com/paper/2411.12045/) (2024)
- [On the Internet, Nobody Knows You're an LLM Bot: Unmasking Web Agents with Multi-Layer Fingerprinting](https://mlsanity.com/paper/2606.30119/) (2026)
- [CPU-Auth: Device Fingerprinting for Authentication via DVFS Side-Channel](https://mlsanity.com/paper/2610.10766/) (2026)

## Abstract (arXiv)

Browser fingerprinting aims to identify users or their devices, through scripts that execute in the users' browser and collect information on software or hardware characteristics. It is used to track users or as an additional means of identification to improve security. In this paper, we report on a new technique that can significantly extend the tracking time of fingerprint-based tracking methods. Our technique, which we call DrawnApart, is a new GPU fingerprinting technique that identifies a device based on the unique properties of its GPU stack. Specifically, we show that variations in speed among the multiple execution units that comprise a GPU can serve as a reliable and robust device signature, which can be collected using unprivileged JavaScript. We investigate the accuracy of DrawnApart under two scenarios. In the first scenario, our controlled experiments confirm that the technique is effective in distinguishing devices with similar hardware and software configurations, even when they are considered identical by current state-of-the-art fingerprinting algorithms. In the second scenario, we integrate a one-shot learning version of our technique into a state-of-the-art browser fingerprint tracking algorithm. We verify our technique through a large-scale experiment involving data collected from over 2,500 crowd-sourced devices over a period of several months and show it provides a boost of up to 67% to the median tracking duration, compared to the state-of-the-art method. DrawnApart makes two contributions to the state of the art in browser fingerprinting. On the conceptual front, it is the first work that explores the manufacturing differences between identical GPUs and the first to exploit these differences in a privacy context. On the practical front, it demonstrates a robust technique for distinguishing between machines with identical hardware and software configurations.