# SEBA: Strong Evaluation of Biometric Anonymizations

arXiv: https://arxiv.org/abs/2407.06648 · HTML: https://mlsanity.com/paper/2407.06648/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 9 Jul 2024.

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

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [Stripformer: Strip Transformer for Fast Image Deblurring](https://mlsanity.com/paper/2204.04627/) (2022)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks](https://mlsanity.com/paper/2005.09544/) (2020)
- [Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation](https://mlsanity.com/paper/2003.13063/) (2020)
- [Introducing the VoicePrivacy Initiative](https://mlsanity.com/paper/2005.01387/) (2020)

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

- [MultiGait: A Multi-Sensor Multi-Perspective Multi-Session Biometric Inference Benchmark and its Dataset](https://mlsanity.com/paper/2609.01036/) (2026)

## Abstract (arXiv)

Biometric data is pervasively captured and analyzed. Using modern machine learning approaches, identity and attribute inferences attacks have proven high accuracy. Anonymizations aim to mitigate such disclosures by modifying data in a way that prevents identification. However, the effectiveness of some anonymizations is unclear. Therefore, improvements of the corresponding evaluation methodology have been proposed recently. In this paper, we introduce SEBA, a framework for strong evaluation of biometric anonymizations. It combines and implements the state-of-the-art methodology in an easy-to-use and easy-to-expand software framework. This allows anonymization designers to easily test their techniques using a strong evaluation methodology. As part of this discourse, we introduce and discuss new metrics that allow for a more straightforward evaluation of the privacy-utility trade-off that is inherent to anonymization attempts. Finally, we report on a prototypical experiment to demonstrate SEBA's applicability.