Audio deepfake detection (ADD) is essential for preventing the misuse of
synthetic voices that may infringe on personal rights and privacy. Recent
zero-shot text-to-speech (TTS) models pose higher risks as they can clone
voices with a single utterance. However, the existing ADD datasets are
outdated, leading to suboptimal generalization of detection models. In this
paper, we construct a new cross-domain ADD dataset comprising over 300 hours of
speech data that is generated by five advanced zero-shot TTS models. To
simulate real-world scenarios, we employ diverse attack methods and audio
prompts from different datasets. Experiments show that, through novel
attack-augmented training, the Wav2Vec2-large and Whisper-medium models achieve
equal error rates of 4.1\% and 6.5\% respectively. Additionally, we demonstrate
our models' outstanding few-shot ADD ability by fine-tuning with just one
minute of target-domain data. Nonetheless, neural codec compressors greatly
affect the detection accuracy, necessitating further research.

通过使用五种先进的零样本文本转语音模型生成超过 300 小时的语音数据，构建了一个新的跨领域的 ADD 数据集。通过新颖的攻击增强训练方法，Wav2Vec2-large 模型和 Whisper-medium 模型分别获得了 4.1％和 6.5％的等误差率，展示了出色的少样本 ADD 能力。然而，神经编解码器压缩技术对检测准确性产生重大影响，需要进一步研究。

跨领域音频深度伪造检测：数据集与分析

Cross-Domain Audio Deepfake Detection: Dataset and Analysis

Autonomous driving systems require many images for analyzing the surrounding
environment. However, there is fewer data protection for private information
among these captured images, such as pedestrian faces or vehicle license
plates, which has become a significant issue. In this paper, in response to the
call for data security laws and regulations and based on the advantages of
large Field of View(FoV) of the fisheye camera, we build the first Autopilot
Desensitization Dataset, called ADD, and formulate the first
deep-learning-based image desensitization framework, to promote the study of
image desensitization in autonomous driving scenarios. The compiled dataset
consists of 650K images, including different face and vehicle license plate
information captured by the surround-view fisheye camera. It covers various
autonomous driving scenarios, including diverse facial characteristics and
license plate colors. Then, we propose an efficient multitask desensitization
network called DesCenterNet as a benchmark on the ADD dataset, which can
perform face and vehicle license plate detection and desensitization tasks.
Based on ADD, we further provide an evaluation criterion for desensitization
performance, and extensive comparison experiments have verified the
effectiveness and superiority of our method on image desensitization.

基于全景鱼眼相机优势，建立了第一个 Autopilot Desensitization Dataset（ADD）并构建了第一个基于深度学习的图像去敏感化框架，促进了自动驾驶场景下图像去敏感化的研究。数据集包含 65 万张图像，包括全景鱼眼相机捕获的各种面部特征和车牌信息，并提供了评估标准和比较实验证明了我们方法在图像去敏感化上的有效性和优越性。