With diverse presentation forgery methods emerging continually, detecting the
authenticity of images has drawn growing attention. Although existing methods
have achieved impressive accuracy in training dataset detection, they still
perform poorly in the unseen domain and suffer from forgery of irrelevant
information such as background and identity, affecting generalizability. To
solve this problem, we proposed a novel framework Selective Domain-Invariant
Feature (SDIF), which reduces the sensitivity to face forgery by fusing content
features and styles. Specifically, we first use a Farthest-Point Sampling (FPS)
training strategy to construct a task-relevant style sample representation
space for fusing with content features. Then, we propose a dynamic feature
extraction module to generate features with diverse styles to improve the
performance and effectiveness of the feature extractor. Finally, a domain
separation strategy is used to retain domain-related features to help
distinguish between real and fake faces. Both qualitative and quantitative
results in existing benchmarks and proposals demonstrate the effectiveness of
our approach.

提出了一种名为 Selective Domain-Invariant Feature (SDIF) 的新型框架，通过融合内容特征和样式来减少对面部伪造的敏感性，并使用动态特征提取模块生成具有多样样式的特征，最终使用领域分离策略来帮助区分真实和伪造的面部。在现有基准和提案中的定性和定量结果证明了我们方法的有效性。

选择性域不变特征用于可推广的深假识别

Selective Domain-Invariant Feature for Generalizable Deepfake Detection

Every recent image-to-image translation model inherently requires either
image-level (i.e. input-output pairs) or set-level (i.e. domain labels)
supervision. However, even set-level supervision can be a severe bottleneck for
data collection in practice. In this paper, we tackle image-to-image
translation in a fully unsupervised setting, i.e., neither paired images nor
domain labels. To this end, we propose a truly unsupervised image-to-image
translation model (TUNIT) that simultaneously learns to separate image domains
and translates input images into the estimated domains. Experimental results
show that our model achieves comparable or even better performance than the
set-level supervised model trained with full labels, generalizes well on
various datasets, and is robust against the choice of hyperparameters (e.g. the
preset number of pseudo domains). Furthermore, TUNIT can be easily extended to
semi-supervised learning with a few labeled data.

本文提出了一种针对图像翻译的无监督学习模型，TUNIT，该模型可以在没有成对数据或领域标签的情况下进行图像 - 图像翻译，同时在各种数据集上表现出可比甚至更好的性能。此外，该模型还可以轻松扩展到半监督学习。