# Swap It Like Its Hot: Segmentation-based spoof attacks on eye-tracking images

arXiv: https://arxiv.org/abs/2404.13827 · HTML: https://mlsanity.com/paper/2404.13827/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 22 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [Pupil: An Open Source Platform for Pervasive Eye Tracking and Mobile Gaze-based Interaction](https://mlsanity.com/paper/1405.0006/) (2014)
- [Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era](https://mlsanity.com/paper/2202.10673/) (2022)
- [Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art](https://mlsanity.com/paper/1804.00194/) (2018)
- [Robust Spatial-spread Deep Neural Image Watermarking](https://mlsanity.com/paper/2005.11735/) (2020)
- [Near-infrared and visible-light periocular recognition with Gabor features using frequency-adaptive automatic eye detection](https://mlsanity.com/paper/2211.05544/) (2022)
- [The Security-Utility Trade-off for Iris Authentication and Eye Animation for Social Virtual Avatars](https://mlsanity.com/paper/2003.04250/) (2020)
- [Privacy-Preserving Eye Videos using Rubber Sheet Model](https://mlsanity.com/paper/2004.01792/) (2020)

## Built on by (most cited first, 2 of 2)

- [Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer](https://mlsanity.com/paper/2503.04707/) (2025)
- [Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study](https://mlsanity.com/paper/2504.10267/) (2025)

## Abstract (arXiv)

Video-based eye trackers capture the iris biometric and enable authentication to secure user identity. However, biometric authentication is susceptible to spoofing another user's identity through physical or digital manipulation. The current standard to identify physical spoofing attacks on eye-tracking sensors uses liveness detection. Liveness detection classifies gaze data as real or fake, which is sufficient to detect physical presentation attacks. However, such defenses cannot detect a spoofing attack when real eye image inputs are digitally manipulated to swap the iris pattern of another person. We propose IrisSwap as a novel attack on gaze-based liveness detection. IrisSwap allows attackers to segment and digitally swap in a victim's iris pattern to fool iris authentication. Both offline and online attacks produce gaze data that deceives the current state-of-the-art defense models at rates up to 58% and motivates the need to develop more advanced authentication methods for eye trackers.