Deepfake videos are becoming increasingly realistic, showing subtle tampering
traces on facial areasthat vary between frames. Consequently, many existing
Deepfake detection methods struggle to detect unknown domain Deepfake videos
while accurately locating the tampered region. To address thislimitation, we
propose Delocate, a novel Deepfake detection model that can both recognize
andlocalize unknown domain Deepfake videos. Ourmethod consists of two stages
named recoveringand localization. In the recovering stage, the modelrandomly
masks regions of interest (ROIs) and reconstructs real faces without tampering
traces, resulting in a relatively good recovery effect for realfaces and a poor
recovery effect for fake faces. Inthe localization stage, the output of the
recoveryphase and the forgery ground truth mask serve assupervision to guide
the forgery localization process. This process strategically emphasizes the
recovery phase of fake faces with poor recovery, facilitating the localization
of tampered regions. Ourextensive experiments on four widely used benchmark
datasets demonstrate that Delocate not onlyexcels in localizing tampered areas
but also enhances cross-domain detection performance.

提出了一种名为 Delocate 的新型深度伪造检测模型，它能够识别和定位未知领域的深度伪造视频，通过恢复和定位两个阶段的方法，对于面部伪造区域的定位表现出较好的效果，并在跨领域检测性能上有所提升。

Delocate：随机定位篡改痕迹的 Deepfake 视频检测和定位

Delocate: Detection and Localization for Deepfake Videos with  Randomly-Located Tampered Traces

Open-World Recognition (OWR) is an emerging field that makes a machine
learning model competent in rejecting the unknowns, managing them, and
incrementally adding novel samples to the base knowledge. However, this broad
objective is not practical for an agent that works on a specific task. Not all
rejected samples will be used for learning continually in the future. Some
novel images in the open environment may not belong to the domain of interest.
Hence, identifying the unknown in the domain of interest is essential for a
machine learning model to learn merely the important samples. In this study, we
propose an evaluation protocol for estimating a model's capability in
separating unknown in-domain (ID) and unknown out-of-domain (OOD). We evaluated
using three approaches with an unknown domain and demonstrated the possibility
of identifying the domain of interest using the pre-trained parameters through
traditional transfer learning, Automated Machine Learning (AutoML), and Nearest
Class Mean (NCM) classifier with First Integer Neighbor Clustering Hierarchy
(FINCH). We experimented with five different domains: garbage, food, dogs,
plants, and birds. The results show that all approaches can be used as an
initial baseline yielding a good accuracy. In addition, a Balanced Accuracy
(BACCU) score from a pre-trained model indicates a tendency to excel in one or
more domains of interest. We observed that MobileNetV3 yielded the highest
BACCU score for the garbage domain and surpassed complex models such as the
transformer network. Meanwhile, our results also suggest that a strong
representation in the pre-trained model is important for identifying unknown
classes in the same domain. This study could open the bridge toward open-world
recognition in domain-specific tasks where the relevancy of the unknown classes
is vital.

开放环境识别（OWR）是一个新兴领域，使得机器学习模型能够拒绝未知样本，并进行管理，逐步将新样本添加到基础知识。本研究提出了一个评估协议，用于估计模型在内域未知类和外域未知类之间分离能力，通过传统迁移学习、自动化机器学习（AutoML）和最近类均值（NCM）分类器与 First Integer Neighbor Clustering Hierarchy（FINCH）相结合的方法，通过对垃圾、食品、狗、植物和鸟类等五个不同领域进行实验，结果表明所有方法都可以作为一个良好的准确性基线，并且预训练模型的平衡准确率（BACCU）得分有可能在一个或多个感兴趣领域中表现出色，同时强调了预训练模型中的有效表示对于识别相同领域的未知类很重要，进一步拓展了开放环境识别在领域特定任务中的应用前景。