RuleBoost: A Neuro-Symbolic Framework for Robust Deepfake Detection

The proliferation of user-friendly deepfake creation tools poses a serious challenge, demanding robust and adaptable detection strategies. Existing approaches primarily focus on raw data analysis or identifying learned artifacts or manual data-driven rules resulting in the mis-classification of deepfakes with distorted facial poses. These architectures also neglect the potential power of combining learned visual features with explicit rules.To address this gap, we introduce RuleBoost, a novel NeuroSymbolic AI based framework that seamlessly fuses extracted visual features with automatically learned rules. Our framework employs a scalable rule-based learning approach to extract learned rules from facial geometry such as distance, area, and angle. The extracted rules integrated with deep visual features show promising results giving state-of-the-art area-under-the-curve of 96.19% and 95.44% on WLDR and FaceForensics++ Datasets respectively, surpassing other deep learning specific methods. To figure out the difference NeuroSymbolic approach makes, we also analyze the samples misclassified by traditional DL-based architectures and correctly classified by Rule- Boosted architecture. Based on empirical evidence, we conclude that DL-based architectures struggle to accurately detect real and fake samples when facial artifacts lead to poses that deviate from standard facial positioning, while RuleBoost exhibits improved performance in the same scenario.