Adversarially Robust Deepfake Detection via Adversarial Feature
  Similarity Learning
Sarwar Khan
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.