The rapid evolution of generative adversarial networks (GANs) and diffusion models has made synthetic media increasingly realistic, raising societal concerns around misinformation, identity fraud, and digital trust. Existing deepfake detection methods either rely on deep learning, which suffers from poor generalization and vulnerability to distortions, or forensic analysis, which is interpretable but limited against new manipulation techniques. This study proposes a hybrid framework that fuses forensic features, including noise residuals, JPEG compression traces, and frequency-domain descriptors, with deep learning representations from convolutional neural networks (CNNs) and vision transformers (ViTs). Evaluated on benchmark datasets (FaceForensics++, Celeb-DF v2, DFDC), the proposed model consistently outperformed single-method baselines and demonstrated superior performance compared to existing state-of-the-art hybrid approaches, achieving F1-scores of 0.96, 0.82, and 0.77, respectively. Robustness tests demonstrated stable performance under compression (F1 = 0.87 at QF = 50), adversarial perturbations (AUC = 0.84), and unseen manipulations (F1 = 0.79). Importantly, explainability analysis showed that Grad-CAM and forensic heatmaps overlapped with ground-truth manipulated regions in 82 percent of cases, enhancing transparency and user trust. These findings confirm that hybrid approaches provide a balanced solution, combining the adaptability of deep models with the interpretability of forensic cues, to develop resilient and trustworthy deepfake detection systems.

翻译：生成对抗网络（GANs）和扩散模型的快速发展使得合成媒体日益逼真，引发了关于虚假信息、身份欺诈和数字信任的社会担忧。现有的深度伪造检测方法要么依赖深度学习（其泛化能力差且易受失真影响），要么依赖取证分析（其可解释性强但面对新型篡改技术时能力有限）。本研究提出了一种混合框架，将取证特征（包括噪声残差、JPEG压缩痕迹和频域描述符）与来自卷积神经网络（CNNs）和视觉变换器（ViTs）的深度学习表征相融合。在基准数据集（FaceForensics++、Celeb-DF v2、DFDC）上的评估表明，所提出的模型始终优于单一方法的基线，并在与现有最先进的混合方法比较中表现出更优的性能，分别实现了0.96、0.82和0.77的F1分数。鲁棒性测试表明，该模型在压缩（QF=50时F1=0.87）、对抗性扰动（AUC=0.84）和未见过的篡改（F1=0.79）下均保持稳定的性能。重要的是，可解释性分析显示，在82%的情况下，Grad-CAM和取证热图与真实篡改区域重叠，从而增强了透明度和用户信任。这些发现证实，混合方法提供了一种平衡的解决方案，将深度模型的适应性与取证线索的可解释性相结合，以开发具有韧性和可信赖的深度伪造检测系统。