Dr. Hafiz Imtiaz

Detecting deepfakes is increasingly important, but it is difficult because deepfakes can be created in many different ways. In the 2025 IEEE Signal Processing Cup, the dataset included many types of deepfake images but had a large imbalance between real and fake samples. To address this, we developed a multistage training approach using CAE‑Net, an ensemble model that combines three architectures: EfficientNet, a Data‑Efficient Image Transformer, and ConvNeXt with wavelet transforms. Our method achieved 94.63% accuracy and 97.37% AUC on the competition’s validation set. This work led to this Journal of Visual Communication and Image Representation publication.