Facial Forgery-based Deepfake Detection using Fine-Grained Features
Aakash Varma Nadimpalli Ajita Rattani
Facial forgery by deepfakes has caused major security risks and raised severe
societal concerns. As a countermeasure, a number of deepfake detection methods
have been proposed. Most of them model deepfake detection as a binary
classification problem using a backbone convolutional neural network (CNN)
architecture pretrained for the task. These CNN-based methods have demonstrated
very high efficacy in deepfake detection with the Area under the Curve (AUC) as
high as $0.99$. However, the performance of these methods degrades
significantly when evaluated across datasets and deepfake manipulation
techniques. This draws our attention towards learning more subtle, local, and
discriminative features for deepfake detection. In this paper, we formulate
deepfake detection as a fine-grained classification problem and propose a new
fine-grained solution to it. Specifically, our method is based on learning
subtle and generalizable features by effectively suppressing background noise
and learning discriminative features at various scales for deepfake detection.
Through extensive experimental validation, we demonstrate the superiority of
our method over the published research in cross-dataset and cross-manipulation
generalization of deepfake detectors for the majority of the experimental
scenarios.