Deepfake technology has raised concerns about the authenticity of digital
content, necessitating the development of effective detection methods. However,
the widespread availability of deepfakes has given rise to a new challenge in
the form of adversarial attacks. Adversaries can manipulate deepfake videos
with small, imperceptible perturbations that can deceive the detection models
into producing incorrect outputs. To tackle this critical issue, we introduce
Adversarial Feature Similarity Learning (AFSL), which integrates three
fundamental deep feature learning paradigms. By optimizing the similarity
between samples and weight vectors, our approach aims to distinguish between
real and fake instances. Additionally, we aim to maximize the similarity
between both adversarially perturbed examples and unperturbed examples,
regardless of their real or fake nature. Moreover, we introduce a
regularization technique that maximizes the dissimilarity between real and fake
samples, ensuring a clear separation between these two categories. With
extensive experiments on popular deepfake datasets, including FaceForensics++,
FaceShifter, and DeeperForensics, the proposed method outperforms other
standard adversarial training-based defense methods significantly. This further
demonstrates the effectiveness of our approach to protecting deepfake detectors
from adversarial attacks.

深度伪造技术引发了对数字内容真实性的担忧，需要开发有效的检测方法。然而，深伪技术的广泛应用带来了新的挑战，即敌对攻击。本文介绍了对抗特征相似性学习（AFSL）方法，该方法整合了三个基本的深度特征学习范例，旨在区分真实和伪造实例，并最大化对抗扰动和未扰动实例之间的相似性。此外，我们引入了一种正则化技术，最大化了真实和伪造样本之间的不相似性，确保了这两个类别的明确分离。通过在 FaceForensics++、FaceShifter 和 DeeperForensics 等流行的深伪数据集上进行大量实验，我们的方法明显优于其他标准对抗训练防御方法，这进一步证明了我们的方法在保护深伪检测器免受敌对攻击方面的有效性。