The rapid development of face forgery technology
has posed a significant threat to information security. While
deepfake detection has proven to be an effective countermeasure,
it often struggles to detect fake images generated by unknown
forgery methods. Thus, the generalization ability of deepfake
detectors to unseen forgery data is a critical concern. Despite
many efforts aimed at discovering new forgery artifacts, they
often fail to generalize to new manipulation technologies. In this
paper, we tackle this challenge by focusing on the difference in
texture patterns between training forgeries and unseen forgeries,
which can lead to a degradation of generalization. Based on
this principle, we propose a new conjecture that encourages
deepfake detectors to reduce their sensitivity to forgery texture
patterns, thereby improving the detection performance. To this
end, we introduce an additional gradient regularization term to
the original empirical loss during training. However, computing
the Hessian matrix in the gradient calculation process of the
regularization term poses a computational complexity. In order
to overcome this issue, we optimize the formulation of the
gradient regularization term using a first-order approximation
method based on Taylor expansion and design a Perturbation
Injection Module (PIM) to simplify the implementation pro-
cess. Additionally, we provide a theoretical analysis from an
optimization perspective and explore an interesting aspect of
our method. Extensive experiments demonstrate the effectiveness
of our approach in improving the generalization ability of
deepfake detectors. Importantly, our method is orthogonal to
recent advancements in powerful backbones and training data
augmentation techniques. When combined with other effective
techniques, our method achieves state-of-the-art experimental
results.