For speaker recognition, it is difficult to extract an accurate speaker
representation from speech because of its mixture of speaker traits and
content. This paper proposes a disentanglement framework that simultaneously
models speaker traits and content variability in speech. It is realized with
the use of three Gaussian inference layers, each consisting of a learnable
transition model that extracts distinct speech components. Notably, a
strengthened transition model is specifically designed to model complex speech
dynamics. We also propose a self-supervision method to dynamically disentangle
content without the use of labels other than speaker identities. The efficacy
of the proposed framework is validated via experiments conducted on the
VoxCeleb and SITW datasets with 9.56% and 8.24% average reductions in EER and
minDCF, respectively. Since neither additional model training nor data is
specifically needed, it is easily applicable in practical use.

该研究提出了一种能同时模拟语音中的说话人特征和内容可变性的解缠结构框架，并通过使用三个高斯推理层实现，其中每个层由可学习的转换模型组成，能够提取出独特的语音组成成分。通过在 VoxCeleb 和 SITW 数据集上进行实验证实了该框架的有效性，其表现为等价错误率和最小 DCF 分别降低了 9.56％和 8.24％，而不需要额外的模型训练或数据，因此可以在实际应用中容易使用。

自我监督解开说话人确认中的声音和内容

Disentangling Voice and Content with Self-Supervision for Speaker  Recognition

Deepfake detection remains a challenging task due to the difficulty of
generalizing to new types of forgeries. This problem primarily stems from the
overfitting of existing detection methods to forgery-irrelevant features and
method-specific patterns. The latter is often ignored by previous works. This
paper presents a novel approach to address the two types of overfitting issues
by uncovering common forgery features. Specifically, we first propose a
disentanglement framework that decomposes image information into three distinct
components: forgery-irrelevant, method-specific forgery, and common forgery
features. To ensure the decoupling of method-specific and common forgery
features, a multi-task learning strategy is employed, including a multi-class
classification that predicts the category of the forgery method and a binary
classification that distinguishes the real from the fake. Additionally, a
conditional decoder is designed to utilize forgery features as a condition
along with forgery-irrelevant features to generate reconstructed images.
Furthermore, a contrastive regularization technique is proposed to encourage
the disentanglement of the common and specific forgery features. Ultimately, we
only utilize the common forgery features for the purpose of generalizable
deepfake detection. Extensive evaluations demonstrate that our framework can
perform superior generalization than current state-of-the-art methods.

本篇论文通过「分离」视觉信息的方法，提出了一种有效解决深度超假的问题的框架，着重考虑了针对具体伪造方法（网络结构、训练数据等）的「过拟合」问题以及如何发现「通用伪造特征」作为泛化的基础。

UCF: 揭示通用深度伪造检测的共同特征

UCF: Uncovering Common Features for Generalizable Deepfake Detection

Set-based face recognition (SFR) aims to recognize the face sets in the
unconstrained scenario, where the appearance of same identity may change
dramatically with extreme variances (e.g., illumination, pose, expression). We
argue that the two crucial issues in SFR, the face quality and burstiness, are
both identity-irrelevant and variance-relevant. The quality and burstiness
assessment are interfered with by the entanglement of identity, and the face
recognition is interfered with by the entanglement of variance. Thus we propose
to separate the identity features with the variance features in a
light-weighted set-based disentanglement framework. Beyond disentanglement, the
variance features are fully utilized to indicate face quality and burstiness in
a set, rather than being discarded after training. To suppress face burstiness
in the sets, we propose a vocabulary-based burst suppression (VBS) method which
quantizes faces with a reference vocabulary. With interword and intra-word
normalization operations on the assignment scores, the face burtisness degrees
are appropriately estimated. The extensive illustrations and experiments
demonstrate the effect of the disentanglement framework with VBS, which gets
new state-of-the-art on the SFR benchmarks. The code will be released at
this https URL

提出了一种轻量级的基于集合的解缠框架，通过将身份特征与方差特征分离来识别面部集合中的质量和突发性，同时还使用基于词汇的突发抑制方法来压制集合中的突发性，实验表明这种方法在 SFR 基准测试中取得了最新的最优结果。