AdaForensics：学习一种具有特征感知能力的自适应深度伪造检测器

中文摘要 AI 辅助

本文提出一种名为AdaForensics的、具有特征感知能力的自适应网络，用于深度伪造检测。现有大多数方法基于精心设计的网络架构学习固定网络以检测深度伪造，但这些方法对所有图像采用相同的深度伪造检测器，未考虑不同人脸特征的差异，无法为不同个体提供定制化的伪造检测。为解决该问题，我们的AdaForensics同时学习与特征无关和与特征相关的嵌入，检测器通过我们设计的超网络动态适应不同人脸。更具体地说，我们的AdaForensics不仅从各类深度伪造图像中探索可共享的抽象特征，还在测试时根据给定特征调整检测器。为实现这一点，我们提出一种双分支超网络来学习自适应深度伪造检测器，该检测器会根据输入特征自动调整参数。在FaceForensics、Celeb-DF和DFDC等广泛使用的数据集上开展的大量实验表明，AdaForensics的性能优于现有最先进的方法。

英文摘要

In this paper, we propose a characteristic-aware adaptive network named AdaForensics for deepfake detection. Most existing methods learn a fixed network to detect deepfakes based on carefully-designed network architectures. However, these methods employ the same deepfake detector for all the images despite of various facial characteristic, which fail to provide customized forgery detection for different individuals. To address this, our AdaForensics simultaneously learns characteristic-agnostic and characteristic-specific embeddings, where the detector dynamically adapts to varying faces with our designed hypernetwork on the fly. More specifically, our AdaForensics not only explores the shareable abstractions from various deepfake images, but also adapts the detector to the given characteristic at test time. To achieve this, we propose a two-branch HyperNetwork to learn an adaptive deepfake detector, which automatically adjusts the parameters based on characteristic of the input. Extensive experiments on widely-used datasets including FaceForensics, Celeb-DF and DFDC demonstrate our AdaForensics outperforms the state-of-the-art works.

发表机构

• Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
• Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）

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