{"ID":2837610,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19187","arxiv_id":"2511.19187","title":"SpectraNet: FFT-assisted Deep Learning Classifier for Deepfake Face Detection","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.19187","url_pdf":"https://arxiv.org/pdf/2511.19187v1","authors":"[\"Nithira Jayarathne\",\"Naveen Basnayake\",\"Keshawa Jayasundara\",\"Pasindu Dodampegama\",\"Praveen Wijesinghe\",\"Hirushika Pelagewatta\",\"Kavishka Abeywardana\",\"Sandushan Ranaweera\",\"Chamira Edussooriya\"]","published":"2025-11-24T14:54:00Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false}