Ensemble-Based Deepfake Detection using State-of-the-Art Models with Robust Cross-Dataset Generalisation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908440967774208 |
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| author | Wahab, Haroon Ugail, Hassan Jaleel, Lujain |
| author_facet | Wahab, Haroon Ugail, Hassan Jaleel, Lujain |
| contents | Machine learning-based Deepfake detection models have achieved impressive results on benchmark datasets, yet their performance often deteriorates significantly when evaluated on out-of-distribution data. In this work, we investigate an ensemble-based approach for improving the generalization of deepfake detection systems across diverse datasets. Building on a recent open-source benchmark, we combine prediction probabilities from several state-of-the-art asymmetric models proposed at top venues. Our experiments span two distinct out-of-domain datasets and demonstrate that no single model consistently outperforms others across settings. In contrast, ensemble-based predictions provide more stable and reliable performance in all scenarios. Our results suggest that asymmetric ensembling offers a robust and scalable solution for real-world deepfake detection where prior knowledge of forgery type or quality is often unavailable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ensemble-Based Deepfake Detection using State-of-the-Art Models with Robust Cross-Dataset Generalisation Wahab, Haroon Ugail, Hassan Jaleel, Lujain Computer Vision and Pattern Recognition Machine learning-based Deepfake detection models have achieved impressive results on benchmark datasets, yet their performance often deteriorates significantly when evaluated on out-of-distribution data. In this work, we investigate an ensemble-based approach for improving the generalization of deepfake detection systems across diverse datasets. Building on a recent open-source benchmark, we combine prediction probabilities from several state-of-the-art asymmetric models proposed at top venues. Our experiments span two distinct out-of-domain datasets and demonstrate that no single model consistently outperforms others across settings. In contrast, ensemble-based predictions provide more stable and reliable performance in all scenarios. Our results suggest that asymmetric ensembling offers a robust and scalable solution for real-world deepfake detection where prior knowledge of forgery type or quality is often unavailable. |
| title | Ensemble-Based Deepfake Detection using State-of-the-Art Models with Robust Cross-Dataset Generalisation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.05996 |