Data augmentation is widely used to mitigate data bias in the training
dataset. However, data augmentation exposes machine learning models to privacy
attacks, such as membership inference attacks. In this paper, we propose an
effective combination of data augmentation and machine unlearning, which can
reduce data bias while providing a provable defense against known attacks.
Specifically, we maintain the fairness of the trained model with
diffusion-based data augmentation, and then utilize multi-shard unlearning to
remove identifying information of original data from the ML model for
protection against privacy attacks. Experimental evaluation across diverse
datasets demonstrates that our approach can achieve significant improvements in
bias reduction as well as robustness against state-of-the-art privacy attacks.

我们提出了一种有效的数据增强和机器遗忘的组合方法，可以减少数据偏差并提供对已知攻击的可证明防御。具体而言，我们利用基于扩散的数据增强来维护训练模型的公平性，并利用多分片遗忘来从机器学习模型中删除原始数据的识别信息，以保护隐私免受攻击。通过对不同数据集的实验评估表明，我们的方法在减少偏差和抵抗最新隐私攻击方面取得了显著改进。

隐私保护的数据增强与机器遗忘实现的去偏移

Privacy-Preserving Debiasing using Data Augmentation and Machine  Unlearning

We present MosaicFusion, a simple yet effective diffusion-based data
augmentation approach for large vocabulary instance segmentation. Our method is
training-free and does not rely on any label supervision. Two key designs
enable us to employ an off-the-shelf text-to-image diffusion model as a useful
dataset generator for object instances and mask annotations. First, we divide
an image canvas into several regions and perform a single round of diffusion
process to generate multiple instances simultaneously, conditioning on
different text prompts. Second, we obtain corresponding instance masks by
aggregating cross-attention maps associated with object prompts across layers
and diffusion time steps, followed by simple thresholding and edge-aware
refinement processing. Without bells and whistles, our MosaicFusion can produce
a significant amount of synthetic labeled data for both rare and novel
categories. Experimental results on the challenging LVIS long-tailed and
open-vocabulary benchmarks demonstrate that MosaicFusion can significantly
improve the performance of existing instance segmentation models, especially
for rare and novel categories. Code will be released at
this https URL

MosaicFusion 是一种简单而有效的基于扩散的数据增强方法，用于大词汇实例分割，通过文本到图像扩散模型生成合成标记数据，提高现有实例分割模型在罕见和新颖类别上的性能。