Hybrid Deepfake Image Detection: A Comprehensive Dataset-Driven Approach Integrating Convolutional and Attention Mechanisms with Frequency Domain Features

Effective deepfake detection tools are becoming increasingly essential over the last few years due to the growing usage of deepfakes in unethical practices. There exists a diverse range of deepfake generation techniques, which makes it challenging to develop an accurate universal detection mechanism. The 2025 Signal Processing Cup (DFWild-Cup competition) provided a diverse dataset of deepfake images, which are generated from multiple deepfake image generators, for training machine learning model(s) to emphasize the generalization of deepfake detection. To this end, we proposed an ensemble-based approach that employs three different neural network architectures: a ResNet-34-based architecture, a data-efficient image transformer (DeiT), and an XceptionNet with Wavelet Transform to capture both local and global features of deepfakes. We visualize the specific regions that these models focus for classification using Grad-CAM, and empirically demonstrate the effectiveness of these models in grouping real and fake images into cohesive clusters using t-SNE plots. Individually, the ResNet-34 architecture has achieved 88.9% accuracy, whereas the Xception network and the DeiT architecture have achieved 87.76% and 89.32% accuracy, respectively. With these networks, our weighted ensemble model achieves an excellent accuracy of 93.23% on the validation dataset of the SP Cup 2025 competition. Finally, the confusion matrix and an Area Under the ROC curve of 97.44% further confirm the stability of our proposed method.