# Zero-Shot Generative De-identification: Inversion-Free Flow for Privacy-Preserving Skin Image Analysis

arXiv: https://arxiv.org/abs/2602.00821 · HTML: https://mlsanity.com/paper/2602.00821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://mlsanity.com/paper/2211.09794/) (2022)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models](https://mlsanity.com/paper/2412.08629/) (2024)
- [DeepPrivacy: A Generative Adversarial Network for Face Anonymization](https://mlsanity.com/paper/1909.04538/) (2019)
- [Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion](https://mlsanity.com/paper/2508.01334/) (2025)

## Abstract (arXiv)

The secure analysis of dermatological images in clinical environments is fundamentally restricted by the critical trade-off between patient privacy and the preservation of diagnostic fidelity. Traditional de-identification techniques often degrade essential pathological markers, while state-of-the-art generative approaches typically require computationally intensive inversion processes or extensive task-specific fine-tuning, limiting their feasibility for real-time deployment. This study introduces a zero-shot generative de-identification framework that utilizes an inversion-free pipeline for privacy-preserving medical image analysis. By leveraging Rectified Flow Transformers (FlowEdit), the proposed method achieves high-fidelity identity transformation in less than 20 seconds without requiring pathology-specific training or labeled datasets. We introduce a novel "segment-by-synthesis" mechanism that generates counterfactual "healthy" and "pathological" digital twin pairs to isolate clinical signals from biometric identifiers in a zero-shot manner. Our approach specifically utilizes the CIELAB color space to decouple erythema-related pathological signals from semantic noise and individual skin characteristics. Pilot validation on high-resolution clinical samples demonstrates robust stability in preserving pathological features, achieving an Intersection over Union (IoU) stability exceeding 0.67, while ensuring rigorous de-identification. These results suggest that the proposed zero-shot, inversion-free approach provides a scalable and efficient solution for secure data sharing and collaborative biomedical research, bypassing the need for large-scale annotated medical datasets while aligning with data protection standards.