Recent DeepFake detection methods have shown excellent performance on public
datasets but are significantly degraded on new forgeries. Solving this problem
is important, as new forgeries emerge daily with the continuously evolving
generative techniques. Many efforts have been made for this issue by seeking
the commonly existing traces empirically on data level. In this paper, we
rethink this problem and propose a new solution from the unsupervised domain
adaptation perspective. Our solution, called DomainForensics, aims to transfer
the forgery knowledge from known forgeries to new forgeries. Unlike recent
efforts, our solution does not focus on data view but on learning strategies of
DeepFake detectors to capture the knowledge of new forgeries through the
alignment of domain discrepancies. In particular, unlike the general domain
adaptation methods which consider the knowledge transfer in the semantic class
category, thus having limited application, our approach captures the subtle
forgery traces. We describe a new bi-directional adaptation strategy dedicated
to capturing the forgery knowledge across domains. Specifically, our strategy
considers both forward and backward adaptation, to transfer the forgery
knowledge from the source domain to the target domain in forward adaptation and
then reverse the adaptation from the target domain to the source domain in
backward adaptation. In forward adaptation, we perform supervised training for
the DeepFake detector in the source domain and jointly employ adversarial
feature adaptation to transfer the ability to detect manipulated faces from
known forgeries to new forgeries. In backward adaptation, we further improve
the knowledge transfer by coupling adversarial adaptation with
self-distillation on new forgeries. This enables the detector to expose new
forgery features from unlabeled data and avoid forgetting the known knowledge
of known...

通过无监督领域自适应的新解决方案，DomainForensics，从已知伪造品向新伪造品传递伪造知识，通过领域差异的对齐捕捉新伪造品的知识，既考虑前向适应也考虑后向适应，在前向适应中，通过有监督训练在已知伪造品中提升 DeepFake 检测器的能力，并通过对新伪造品进行对抗特征适应传递伪造识别能力，而在后向适应中，通过对抗适应和自我蒸馏提高知识传递效果，使检测器能够从未标记数据中揭示新伪造特征并避免遗忘已知伪造知识。

领域取证：通过双向适应揭示跨领域的人脸伪造

DomainForensics: Exposing Face Forgery across Domains via Bi-directional  Adaptation

The issue of detecting deepfakes has garnered significant attention in the
research community, with the goal of identifying facial manipulations for abuse
prevention. Although recent studies have focused on developing generalized
models that can detect various types of deepfakes, their performance is not
always be reliable and stable, which poses limitations in real-world
applications. Instead of learning a forgery detector, in this paper, we propose
a novel framework - Integrity Encryptor, aiming to protect portraits in a
proactive strategy. Our methodology involves covertly encoding messages that
are closely associated with key facial attributes into authentic images prior
to their public release. Unlike authentic images, where the hidden messages can
be extracted with precision, manipulating the facial attributes through
deepfake techniques can disrupt the decoding process. Consequently, the
modified facial attributes serve as a mean of detecting manipulated images
through a comparison of the decoded messages. Our encryption approach is
characterized by its brevity and efficiency, and the resulting method exhibits
a good robustness against typical image processing traces, such as image
degradation and noise. When compared to baselines that struggle to detect
deepfakes in a black-box setting, our method utilizing conditional encryption
showcases superior performance when presented with a range of different types
of forgeries. In experiments conducted on our protected data, our approach
outperforms existing state-of-the-art methods by a significant margin.

本文提出了一种新的算法，基于保护肖像实施先发制人的策略，将与关键面部属性密切相关的信息隐秘编码嵌入正式图片中，以便检测被篡改的图像，实验证明，该方法对于各种类型的伪造具有较好的鲁棒性。