Comparative Evaluation and Analysis of Different Deepfake Detectors

Deepfakes, synthetic media generated using deep learning, pose significant risks to the integrity, security, and trust of information. Reliable detection is therefore critical, yet existing models often fail when exposed to real-world distortions such as compression, occlusion, and lighting variations. This paper presents a comparative evaluation of deepfake detection models, including XceptionNet, EfficientNet, MesoNet, and Vision Transformers, across multiple benchmark datasets such as FaceForensics++, DFDC, Celeb-DF, and Wild-Deepfake. Models are assessed not only under pristine conditions but also under controlled distortions that reflect realistic deployment environments. The results show that XceptionNet and the fine-tuned Vision Transformers achieve the strongest accuracy and robustness, maintaining competitive performance across domains, while MesoNet demonstrates computational efficiency but suffers from reduced reliability under challenging conditions. EfficientNet provides a balance between parameter efficiency and detection quality but lags behind in cross-dataset generalization. The findings highlight clear trade-offs between robustness, efficiency, and deployment feasibility, emphasizing that lightweight models are best suited for edge scenarios, whereas more complex architectures remain preferable in cloud or high-resource environments. The study concludes with open challenges and future research directions, including the integration of multimodal cues, domain adaptation, and explainable detection frameworks, to improve resilience against increasingly sophisticated deepfake generation techniques.