Ensemble-Based Deepfake Detection using State-of-the-Art Models with Robust Cross-Dataset Generalisation

• URL: http://arxiv.org/abs/2507.05996v1
• Date: Tue, 08 Jul 2025 13:54:48 GMT
• Title: Ensemble-Based Deepfake Detection using State-of-the-Art Models with Robust Cross-Dataset Generalisation
• Authors: Haroon Wahab, Hassan Ugail, Lujain Jaleel,
• Abstract summary: Machine learning-based Deepfake detection models have achieved impressive results on benchmark datasets.<n>But their performance often deteriorates significantly when evaluated on out-of-distribution data.<n>In this work, we investigate an ensemble-based approach for improving the generalization of deepfake detection systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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