Privacy Protection in MRI Scans Using 3D Masked Autoencoders
- URL: http://arxiv.org/abs/2310.15778v3
- Date: Mon, 18 Mar 2024 13:27:01 GMT
- Title: Privacy Protection in MRI Scans Using 3D Masked Autoencoders
- Authors: Lennart Alexander Van der Goten, Kevin Smith,
- Abstract summary: Data anonymization and de-identification is concerned with ensuring the privacy and confidentiality of individuals' personal information.
We propose CP-MAE, a model that de-identifies the face by remodeling it.
With our method we are able to synthesize high-fidelity scans of resolution up to $2563$ -- compared to $1283$ with previous approaches.
- Score: 2.463789441707266
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: MRI scans provide valuable medical information, however they also contain sensitive and personally identifiable information that needs to be protected. Whereas MRI metadata is easily sanitized, MRI image data is a privacy risk because it contains information to render highly-realistic 3D visualizations of a patient's head, enabling malicious actors to possibly identify the subject by cross-referencing a database. Data anonymization and de-identification is concerned with ensuring the privacy and confidentiality of individuals' personal information. Traditional MRI de-identification methods remove privacy-sensitive parts (e.g. eyes, nose etc.) from a given scan. This comes at the expense of introducing a domain shift that can throw off downstream analyses. In this work, we propose CP-MAE, a model that de-identifies the face by remodeling it (e.g. changing the face) rather than by removing parts using masked autoencoders. CP-MAE outperforms all previous approaches in terms of downstream task performance as well as de-identification. With our method we are able to synthesize high-fidelity scans of resolution up to $256^3$ -- compared to $128^3$ with previous approaches -- which constitutes an eight-fold increase in the number of voxels.
Related papers
- Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential [11.891539513675697]
We develop a refacing pipeline that recovers faces in defaced head MRIs using cascaded probabilistic diffusion models (DPMs)
The DPMs are trained on images from 180 subjects and tested on images from 484 unseen subjects, 469 of whom are from a different dataset.
Results show that DPMs can generate high-fidelity faces that resemble the original faces from defaced images, with surface distances to the original faces significantly smaller than those of a population average face.
arXiv Detail & Related papers (2025-01-31T00:58:12Z) - Medical Manifestation-Aware De-Identification [45.48447211223584]
We release MeMa, consisting of over 40,000 photo-realistic patient faces.
MeMa avoids breaching real patient privacy, while ensuring rich and plausible medical manifestations.
We recruit expert clinicians to annotate MeMa with both coarse- and fine-grained labels.
arXiv Detail & Related papers (2024-12-14T12:09:41Z) - Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
We propose a hardware-level face de-identification method to solve this vulnerability.
We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
arXiv Detail & Related papers (2024-03-31T19:28:04Z) - OpticalDR: A Deep Optical Imaging Model for Privacy-Protective
Depression Recognition [66.91236298878383]
Depression Recognition (DR) poses a considerable challenge, especially in the context of privacy concerns.
We design a new imaging system to erase the identity information of captured facial images while retain disease-relevant features.
It is irreversible for identity information recovery while preserving essential disease-related characteristics necessary for accurate DR.
arXiv Detail & Related papers (2024-02-29T01:20:29Z) - Diff-Privacy: Diffusion-based Face Privacy Protection [58.1021066224765]
In this paper, we propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy.
Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image.
Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding.
arXiv Detail & Related papers (2023-09-11T09:26:07Z) - Deep Learning-based Anonymization of Chest Radiographs: A
Utility-preserving Measure for Patient Privacy [7.240611820374677]
The conventional anonymization process is carried out by obscuring personal information in the images with black boxes.
Such simple measures retain biometric information in the chest radiographs, allowing patients to be re-identified by a linkage attack.
We propose the first deep learning-based approach (PriCheXy-Net) to targetedly anonymize chest radiographs.
arXiv Detail & Related papers (2022-09-23T11:36:32Z) - Privacy-Preserving Face Recognition with Learnable Privacy Budgets in
Frequency Domain [77.8858706250075]
This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain.
Our method performs very well with several classical face recognition test sets.
arXiv Detail & Related papers (2022-07-15T07:15:36Z) - FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders [81.21440457805932]
We propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously.
randomly masked face images are used to train the reconstruction module in FaceMAE.
We also perform sufficient privacy-preserving face recognition on several public face datasets.
arXiv Detail & Related papers (2022-05-23T07:19:42Z) - Conditional De-Identification of 3D Magnetic Resonance Images [29.075173293529947]
We propose a new class of de-identification techniques that, instead of removing facial features, remodels them.
We demonstrate that our approach preserves privacy far better than existing techniques, without compromising downstream medical analyses.
arXiv Detail & Related papers (2021-10-18T15:19:35Z) - Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks.
TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
arXiv Detail & Related papers (2020-03-15T12:45:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.