SpectraNet: FFT-assisted Deep Learning Classifier for Deepfake Face Detection
This work addresses the problem of misinformation by providing an accessible deepfake detection tool for non-experts, though it appears incremental in approach.
The paper tackled deepfake image detection by developing a lightweight binary classification model based on EfficientNet-B6, achieving high accuracy, stability, and generalization through fine-tuning and transformation techniques.
Detecting deepfake images is crucial in combating misinformation. We present a lightweight, generalizable binary classification model based on EfficientNet-B6, fine-tuned with transformation techniques to address severe class imbalances. By leveraging robust preprocessing, oversampling, and optimization strategies, our model achieves high accuracy, stability, and generalization. While incorporating Fourier transform-based phase and amplitude features showed minimal impact, our proposed framework helps non-experts to effectively identify deepfake images, making significant strides toward accessible and reliable deepfake detection.