SpectraNet: FFT-assisted Deep Learning Classifier for Deepfake Face Detection
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911283855491072 |
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| author | Jayarathne, Nithira Basnayake, Naveen Jayasundara, Keshawa Dodampegama, Pasindu Wijesinghe, Praveen Pelagewatta, Hirushika Abeywardana, Kavishka Ranaweera, Sandushan Edussooriya, Chamira |
| author_facet | Jayarathne, Nithira Basnayake, Naveen Jayasundara, Keshawa Dodampegama, Pasindu Wijesinghe, Praveen Pelagewatta, Hirushika Abeywardana, Kavishka Ranaweera, Sandushan Edussooriya, Chamira |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SpectraNet: FFT-assisted Deep Learning Classifier for Deepfake Face Detection Jayarathne, Nithira Basnayake, Naveen Jayasundara, Keshawa Dodampegama, Pasindu Wijesinghe, Praveen Pelagewatta, Hirushika Abeywardana, Kavishka Ranaweera, Sandushan Edussooriya, Chamira Computer Vision and Pattern Recognition Machine Learning I.4.9; I.2.10; I.2.6 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. |
| title | SpectraNet: FFT-assisted Deep Learning Classifier for Deepfake Face Detection |
| topic | Computer Vision and Pattern Recognition Machine Learning I.4.9; I.2.10; I.2.6 |
| url | https://arxiv.org/abs/2511.19187 |