We present a method for expanding a dataset by incorporating knowledge from
the wide distribution of pre-trained latent diffusion models. Data
augmentations typically incorporate inductive biases about the image formation
process into the training (e.g. translation, scaling, colour changes, etc.).
Here, we go beyond simple pixel transformations and introduce the concept of
instance-level data augmentation by repainting parts of the image at the level
of object instances. The method combines a conditional diffusion model with
depth and edge maps control conditioning to seamlessly repaint individual
objects inside the scene, being applicable to any segmentation or detection
dataset. Used as a data augmentation method, it improves the performance and
generalization of the state-of-the-art salient object detection, semantic
segmentation and object detection models. By redrawing all privacy-sensitive
instances (people, license plates, etc.), the method is also applicable for
data anonymization. We also release fully synthetic and anonymized expansions
for popular datasets: COCO, Pascal VOC and DUTS.

本研究提出了一种使用预训练的潜在扩散模型来扩充数据集的方法，并引入了实例级数据增强的概念。该方法通过重新绘制场景内的个别对象来改善卓越的显著目标检测、语义分割和物体检测模型的性能和泛化能力，并在需要数据匿名化时，通过重新绘制隐私敏感实例来实现。

数据集增强与实例级别的数据扩充

Dataset Enhancement with Instance-Level Augmentations

MRI scans provide valuable medical information, however they also contain
sensitive and personally identifiable information (PII) 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. Recently, a GAN-based approach was proposed to de-identify
a patient's scan by remodeling it (e.g. changing the face) rather than by
removing parts. In this work, we propose CP-MAE, a model that de-identifies the
face using masked autoencoders and that outperforms all previous approaches in
terms of downstream task performance as well as de-identification. With our
method we are able to synthesize scans of resolution up to $256^3$ (previously
128 cubic) which constitutes an eight-fold increase in the number of voxels.
Using our construction we were able to design a system that exhibits a highly
robust training stage, making it easy to fit the network on novel data.

利用 CP-MAE 提出的模型，通过遮蔽自动编码器对人脸进行去识别，表现出卓越的下游任务性能和去识别效果，同时能够合成高达 $256^3$ 分辨率的扫描，大大增加了体素的数量。

增强 MRI 扫描的隐私保护的 3D 掩蔽自编码器

3D Masked Autoencoders for Enhanced Privacy in MRI Scans

In recent years, the increasing availability of personal data has raised
concerns regarding privacy and security. One of the critical processes to
address these concerns is data anonymization, which aims to protect individual
privacy and prevent the release of sensitive information. This research focuses
on the importance of face anonymization. Therefore, we introduce
GANonymization, a novel face anonymization framework with facial
expression-preserving abilities. Our approach is based on a high-level
representation of a face which is synthesized into an anonymized version based
on a generative adversarial network (GAN). The effectiveness of the approach
was assessed by evaluating its performance in removing identifiable facial
attributes to increase the anonymity of the given individual face.
Additionally, the performance of preserving facial expressions was evaluated on
several affect recognition datasets and outperformed the state-of-the-art
method in most categories. Finally, our approach was analyzed for its ability
to remove various facial traits, such as jewelry, hair color, and multiple
others. Here, it demonstrated reliable performance in removing these
attributes. Our results suggest that GANonymization is a promising approach for
anonymizing faces while preserving facial expressions.

本研究介绍了 GANonymization，一种基于生成对抗网络的面部匿名化框架，用于保护个人隐私并防止释放敏感信息。该方法被评估为能够移除识别面部属性，同时保持面部表情，是一种很有前途的面部匿名化方法。