Research paper Deep fake misuse prevention solution
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| Autores principales: | , |
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2024
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| _version_ | 1866902145395064832 |
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| author | Velamajala, Amuktha Korlamanda, Naga SubhaSravani |
| author_facet | Velamajala, Amuktha Korlamanda, Naga SubhaSravani |
| contents | <p>This research presents a novel deepfake detection methodology leveraging the combined strengths of GAN-CNN-LSTM architectures and temporal consistency analysis. By integrating the robust spatial feature extraction capabilities of CNNs with the dynamic temporal sequence modeling of LSTMs, and enhancing these with the generative power of GANs, the proposed model demonstrates significant improvements in accuracy and robustness over traditional deepfake detection methods. The GAN-CNN-LSTM approach addresses the limitations of previous techniques that often struggle with the subtle and varied nature of deepfake manipulations.</p> <p>The adaptability of the model to different video formats and its potential for scalability make it a valuable tool in the fight against the growing threat of deepfake technology. While the results are promising, future research can explore incorporating additional data modalities, such as audio and text, to enhance detection capabilities further </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14001725 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Research paper Deep fake misuse prevention solution Velamajala, Amuktha Korlamanda, Naga SubhaSravani Deep learning Artificial intelligence <p>This research presents a novel deepfake detection methodology leveraging the combined strengths of GAN-CNN-LSTM architectures and temporal consistency analysis. By integrating the robust spatial feature extraction capabilities of CNNs with the dynamic temporal sequence modeling of LSTMs, and enhancing these with the generative power of GANs, the proposed model demonstrates significant improvements in accuracy and robustness over traditional deepfake detection methods. The GAN-CNN-LSTM approach addresses the limitations of previous techniques that often struggle with the subtle and varied nature of deepfake manipulations.</p> <p>The adaptability of the model to different video formats and its potential for scalability make it a valuable tool in the fight against the growing threat of deepfake technology. While the results are promising, future research can explore incorporating additional data modalities, such as audio and text, to enhance detection capabilities further </p> |
| title | Research paper Deep fake misuse prevention solution |
| topic | Deep learning Artificial intelligence |
| url | https://doi.org/10.5281/zenodo.14001725 |