The increasing prevalence of audio deepfakes poses significant security
threats, necessitating robust detection methods. While existing detection
systems exhibit promise, their robustness against malicious audio manipulations
remains underexplored. To bridge the gap, we undertake the first comprehensive
study of the susceptibility of the most widely adopted audio deepfake detectors
to manipulation attacks. Surprisingly, even manipulations like volume control
can significantly bypass detection without affecting human perception. To
address this, we propose CLAD (Contrastive Learning-based Audio deepfake
Detector) to enhance the robustness against manipulation attacks. The key idea
is to incorporate contrastive learning to minimize the variations introduced by
manipulations, therefore enhancing detection robustness. Additionally, we
incorporate a length loss, aiming to improve the detection accuracy by
clustering real audios more closely in the feature space. We comprehensively
evaluated the most widely adopted audio deepfake detection models and our
proposed CLAD against various manipulation attacks. The detection models
exhibited vulnerabilities, with FAR rising to 36.69%, 31.23%, and 51.28% under
volume control, fading, and noise injection, respectively. CLAD enhanced
robustness, reducing the FAR to 0.81% under noise injection and consistently
maintaining an FAR below 1.63% across all tests. Our source code and
documentation are available in the artifact repository
(this https URL).

音频深度伪造的广泛应用和威胁需要强大的检测方法。本研究首次全面调查了最常用的音频深伪造检测器对操纵攻击的敏感性，并提出了 CLAD 来改善其鲁棒性。通过对比学习和长度损失等技术，CLAD 显著提高了检测的准确性和鲁棒性。

对抗式学习的鲁棒音频深度伪造检测（CLAD）

CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with  Contrastive Learning

Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a
node is anomalous or not. One common observation we made from previous
unsupervised methods is that they not only assume the absence of such anomaly
labels, but also the absence of class labels (the class a node belongs to used
in a general node classification task). In this work, we study the utility of
class labels for unsupervised GAD; in particular, how they enhance the
detection of structural anomalies. To this end, we propose a Class Label-aware
Graph Anomaly Detection framework (CLAD) that utilizes a limited amount of
labeled nodes to enhance the performance of unsupervised GAD. Extensive
experiments on ten datasets demonstrate the superior performance of CLAD in
comparison to existing unsupervised GAD methods, even in the absence of
ground-truth class label information. The source code for CLAD is available at
https://github.com/jhkim611/CLAD.

研究了无监督的图异常检测方法中类标签的作用，介绍了一个利用有限量带有标签的节点以提升无监督图异常检测性能的 Class Label-aware Graph Anomaly Detection 框架 (CLAD)。实验证明了 CLAD 在无真实类标签信息的情况下相比现有无监督图异常检测方法具有卓越的性能。

类标签感知图异常检测

Class Label-aware Graph Anomaly Detection

Convolutional neural networks (CNNs) have achieved superhuman performance in
multiple vision tasks, especially image classification. However, unlike humans,
CNNs leverage spurious features, such as background information to make
decisions. This tendency creates different problems in terms of robustness or
weak generalization performance. Through our work, we introduce a contrastive
learning-based approach (CLAD) to mitigate the background bias in CNNs. CLAD
encourages semantic focus on object foregrounds and penalizes learning features
from irrelavant backgrounds. Our method also introduces an efficient way of
sampling negative samples. We achieve state-of-the-art results on the
Background Challenge dataset, outperforming the previous benchmark with a
margin of 4.1\%. Our paper shows how CLAD serves as a proof of concept for
debiasing of spurious features, such as background and texture (in
supplementary material).

本篇论文提出了一种基于对比学习的方法，称为 CLAD，用于减轻卷积神经网络中的背景偏差问题，并鼓励对物体前景进行语义关注，惩罚学习与背景无关的特征。通过在 Background Challenge 数据集上的实验，证明该方法超越了以往水平，并为抑制与背景和质地等有关的虚假特征提供了可行性证明。