Model Inversion (MI) attacks aim to recover the private training data from
the target model, which has raised security concerns about the deployment of
DNNs in practice. Recent advances in generative adversarial models have
rendered them particularly effective in MI attacks, primarily due to their
ability to generate high-fidelity and perceptually realistic images that
closely resemble the target data. In this work, we propose a novel Dynamic
Memory Model Inversion Attack (DMMIA) to leverage historically learned
knowledge, which interacts with samples (during the training) to induce diverse
generations. DMMIA constructs two types of prototypes to inject the information
about historically learned knowledge: Intra-class Multicentric Representation
(IMR) representing target-related concepts by multiple learnable prototypes,
and Inter-class Discriminative Representation (IDR) characterizing the
memorized samples as learned prototypes to capture more privacy-related
information. As a result, our DMMIA has a more informative representation,
which brings more diverse and discriminative generated results. Experiments on
multiple benchmarks show that DMMIA performs better than state-of-the-art MI
attack methods.

提出了一种新颖的动态记忆模型逆向攻击（DMMIA），它利用历史学习的知识与样本交互来诱导多样性生成，并通过构建两种类型的原型（被称为 Intra-class Multicentric Representation 和 Inter-class Discriminative Representation）注入关于历史学习知识的信息，从而获得更具多样性和区分性的生成结果。实验证明，DMMIA 攻击方法优于现有的模型逆向攻击方法。

通过动态记忆学习的模型逆推攻击

Model Inversion Attack via Dynamic Memory Learning

Generative adversarial models (GANs) continue to produce advances in terms of
the visual quality of still images, as well as the learning of temporal
correlations. However, few works manage to combine these two interesting
capabilities for the synthesis of video content: Most methods require an
extensive training dataset to learn temporal correlations, while being rather
limited in the resolution and visual quality of their output. We present a
novel approach to the video synthesis problem that helps to greatly improve
visual quality and drastically reduce the amount of training data and resources
necessary for generating videos. Our formulation separates the spatial domain,
in which individual frames are synthesized, from the temporal domain, in which
motion is generated. For the spatial domain we use a pre-trained StyleGAN
network, the latent space of which allows control over the appearance of the
objects it was trained for. The expressive power of this model allows us to
embed our training videos in the StyleGAN latent space. Our temporal
architecture is then trained not on sequences of RGB frames, but on sequences
of StyleGAN latent codes. The advantageous properties of the StyleGAN space
simplify the discovery of temporal correlations. We demonstrate that it
suffices to train our temporal architecture on only 10 minutes of footage of 1
subject for about 6 hours. After training, our model can not only generate new
portrait videos for the training subject, but also for any random subject which
can be embedded in the StyleGAN space.

本研究提出了一种新的视频合成方法，通过将空间域和时间域分离，利用预训练的 StyleGAN 网络的潜在空间进行训练，成功地生成了高质量的人像视频，并显著降低了所需训练数据和资源的数量。

StyleVideoGAN: 使用预训练 StyleGAN 的时间生成模型

StyleVideoGAN: A Temporal Generative Model using a Pretrained StyleGAN

Models that are learned from real-world data are often biased because the
data used to train them is biased. This can propagate systemic human biases
that exist and ultimately lead to inequitable treatment of people, especially
minorities. To characterize bias in learned classifiers, existing approaches
rely on human oracles labeling real-world examples to identify the "blind
spots" of the classifiers; these are ultimately limited due to the human labor
required and the finite nature of existing image examples. We propose a
simulation-based approach for interrogating classifiers using generative
adversarial models in a systematic manner. We incorporate a progressive
conditional generative model for synthesizing photo-realistic facial images and
Bayesian Optimization for an efficient interrogation of independent facial
image classification systems. We show how this approach can be used to
efficiently characterize racial and gender biases in commercial systems.

使用逐步条件生成模型和贝叶斯优化的方法，通过生成对抗网络来分析分类器在种族和性别方面的偏差。