A Hybrid CNN-Ensemble Framework for Robust DeepFake Image Detection

The fast development of deepfake technologies has caused growing concerns related to the authentication of digital media, the integrity of personal identification, and the spreading of disinformation. Therefore, there is a growing need for effective automatic detectors of deepfakes. Meanwhile, existing techniques of deepfake detection encounter a large number of difficulties. First, it is difficult to distinguish the subtle manipulation details of faces. Another problem is poor generalization when applying models to novel datasets or highly realistic, generated synthetic faces. Another issue is low accuracy in the case of imbalanced data, where samples of one class dominate others. Thus, to address those problems, this study proposes a novel hybrid approach based on a combination of deep learning (DL) and conventional machine learning (ML) for detecting deepfake images. More precisely, two pre-trained CNNs (MobileNetV2 and ResNet50) were applied to generate features and classify them via the Random Forest (RF) algorithm. The experiments have been conducted on a benchmark of 6,557 facial images marked as either real or fake. The findings reveal that the MobileNetV2+RF achieved the highest accuracy 99%, followed by MobileNetV2 with 98% and ResNet50 with 97% accuracy. This suggests that the hybrid architecture helps to increase the effectiveness of the solution. A statistical significance test reveals that none of the models' performances differ significantly from each other (p > 0.05). Overall, the proposed system demonstrates excellent metrics concerning accuracy and precision.

@article{Alsulami2026,
  title     = {A Hybrid CNN-Ensemble Framework for Robust DeepFake Image Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mohammad Alsulami},
  doi       = {10.14569/IJACSA.2026.0170644},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170644}
}