Deepfakes, as AI-generated media, have increasingly threatened media
integrity and personal privacy with realistic yet fake digital content. In this
work, we introduce an open-source and user-friendly online platform,
DeepFake-O-Meter v2.0, that integrates state-of-the-art methods for detecting
Deepfake images, videos, and audio. Built upon DeepFake-O-Meter v1.0, we have
made significant upgrades and improvements in platform architecture design,
including user interaction, detector integration, job balancing, and security
management. The platform aims to offer everyday users a convenient service for
analyzing DeepFake media using multiple state-of-the-art detection algorithms.
It ensures secure and private delivery of the analysis results. Furthermore, it
serves as an evaluation and benchmarking platform for researchers in digital
media forensics to compare the performance of multiple algorithms on the same
input. We have also conducted detailed usage analysis based on the collected
data to gain deeper insights into our platform's statistics. This involves
analyzing two-month trends in user activity and evaluating the processing
efficiency of each detector.

我们介绍了一个开源且用户友好的在线平台，DeepFake-O-Meter v2.0，该平台整合了用于检测 Deepfake 图像、视频和音频的最先进方法。该平台以 DeepFake-O-Meter v1.0 为基础，在平台架构设计方面进行了重大升级和改进，包括用户交互、检测器整合、工作平衡和安全管理。该平台旨在为日常用户提供方便的服务，使用多种最先进的检测算法分析 DeepFake 媒体，确保分析结果的安全和私密传递。此外，它还作为一个评估和基准平台，供数字媒体取证研究人员比较多个算法在相同输入上的性能。我们还根据收集的数据进行了详细的使用分析，以深入了解我们平台的统计信息。这包括分析用户活动的两个月趋势和评估每个检测器的处理效率。

DeepFake-O-Meter v2.0：一个用于 DeepFake 检测的开放平台

DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection

With the rapid development of facial manipulation techniques, face forgery
detection has received considerable attention in digital media forensics due to
security concerns. Most existing methods formulate face forgery detection as a
classification problem and utilize binary labels or manipulated region masks as
supervision. However, without considering the correlation between local
regions, these global supervisions are insufficient to learn a generalized
feature and prone to overfitting. To address this issue, we propose a novel
perspective of face forgery detection via local relation learning.
Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which
measures the similarity between features of local regions and forms a robust
and generalized similarity pattern. Moreover, we propose an RGB-Frequency
Attention Module (RFAM) to fuse information in both RGB and frequency domains
for more comprehensive local feature representation, which further improves the
reliability of the similarity pattern. Extensive experiments show that the
proposed method consistently outperforms the state-of-the-arts on widely-used
benchmarks. Furthermore, detailed visualization shows the robustness and
interpretability of our method.

本文提出了一种通过局部关系学习来检测面部伪造的新方法，其中使用了多尺度补丁相似性模块 (MPSM) 来测量局部区域特征的相似性，并形成了一个强大且泛化的相似性模式。此外，本文提出了 RGB-Frequency Attention Module (RFAM) 来融合 RGB 和频率领域的信息进行更全面的局部特征表示，进一步提高了相似性模式的可靠性。广泛的实验证明了该方法在广泛使用的基准测试中始终优于最先进的技术。此外，详细的可视化展示了我们方法的鲁棒性和可解释性。

人脸伪造检测中的局部关系学习

Local Relation Learning for Face Forgery Detection

Detecting manipulated images and videos is an important topic in digital
media forensics. Most detection methods use binary classification to determine
the probability of a query being manipulated. Another important topic is
locating manipulated regions (i.e., performing segmentation), which are mostly
created by three commonly used attacks: removal, copy-move, and splicing. We
have designed a convolutional neural network that uses the multi-task learning
approach to simultaneously detect manipulated images and videos and locate the
manipulated regions for each query. Information gained by performing one task
is shared with the other task and thereby enhance the performance of both
tasks. A semi-supervised learning approach is used to improve the network's
generability. The network includes an encoder and a Y-shaped decoder.
Activation of the encoded features is used for the binary classification. The
output of one branch of the decoder is used for segmenting the manipulated
regions while that of the other branch is used for reconstructing the input,
which helps improve overall performance. Experiments using the FaceForensics
and FaceForensics++ databases demonstrated the network's effectiveness against
facial reenactment attacks and face swapping attacks as well as its ability to
deal with the mismatch condition for previously seen attacks. Moreover,
fine-tuning using just a small amount of data enables the network to deal with
unseen attacks.

本文设计了一个使用多任务学习方法的卷积神经网络用于同时检测和定位查询的篡改图像和视频以及使用半监督学习方法来提高网络的泛化能力，实验表明该网络对面部重现攻击和面部交换攻击很有效，并能处理以前从未见过的攻击。