Meta-Learning for DeepFake Detection: An Ensemble-Based Approach

This study proposes an ensemble-based
deepfake detection framework that combines multiple pretrained
convolutional neural networks (CNNs) using a trainable meta-
classifier. Experiments were conducted using the SDFVD2.0 [1]
dataset containing 927 videos (456 real and 471 fake), from
which approximately 2,400 facial images were extracted and
balanced across classes. Three CNN architectures—ResNet101,
DenseNet201, and GoogLeNet—were fine-tuned for binary clas-
sification. Instead of relying on a single model, their outputs were
fused through a meta-learning classifier to improve robustness
and generalization. The proposed ensemble approach achieved
an accuracy of 98.10% on a validation set of 480 images, with
strong precision (97.50%), recall (98.73%), F1-score (98.11%),
and an AUC of 0.992.