This study provides a thorough evaluation of four distinguished deepfake detection tools—BioID, WeVerify, GLFF, and CLIP-ViT—using the Celeb-DF (v2) dataset in conjunction with several subsidiary subsets. This study emphasized per-class detection rates, error undecidable outcomes, and practical implications in operational scenarios. The findings indicate that while BioID and WeVerify attain near-perfect specificity on actual images (100% and 98.7%, respectively), their sensitivity to manipulated (fake) images remains constrained (31.5% and 23.3%, respectively), resulting in a significant proportion of undetected forgeries. In contrast, GLFF and CLIP-ViT demonstrate divergent profiles when evaluated on restricted subsets, exhibiting moderate success on real images but negligible sensitivity within pure fake subsets. The findings of the study support the use of hybrid detection approaches, which entail the combination of multiple complementary models and training augmentations with realistic degradations. Future research should focus on sample diversity expansion, cross-dataset validation incorporation, and evaluation protocol refinement. These efforts are intended to facilitate the development of reliable and deployable deepfake detection systems in the context of real-world media authentication.