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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2402.14825 |
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| _version_ | 1866910341450956800 |
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| author | Cantero-Arjona, Paloma Sánchez-Macián, Alfonso |
| author_facet | Cantero-Arjona, Paloma Sánchez-Macián, Alfonso |
| contents | The rapid development of technologies and artificial intelligence makes deepfakes an increasingly sophisticated and challenging-to-identify technique. To ensure the accuracy of information and control misinformation and mass manipulation, it is of paramount importance to discover and develop artificial intelligence models that enable the generic detection of forged videos. This work aims to address the detection of deepfakes across various existing datasets in a scenario with limited computing resources. The goal is to analyze the applicability of different deep learning techniques under these restrictions and explore possible approaches to enhance their efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deepfake Detection and the Impact of Limited Computing Capabilities Cantero-Arjona, Paloma Sánchez-Macián, Alfonso Computer Vision and Pattern Recognition Machine Learning Image and Video Processing The rapid development of technologies and artificial intelligence makes deepfakes an increasingly sophisticated and challenging-to-identify technique. To ensure the accuracy of information and control misinformation and mass manipulation, it is of paramount importance to discover and develop artificial intelligence models that enable the generic detection of forged videos. This work aims to address the detection of deepfakes across various existing datasets in a scenario with limited computing resources. The goal is to analyze the applicability of different deep learning techniques under these restrictions and explore possible approaches to enhance their efficiency. |
| title | Deepfake Detection and the Impact of Limited Computing Capabilities |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2402.14825 |