The spread of DeepFake media presents serious threats to digital security and authenticity. In this study, the DenseNet-121 architecture trained on RGB and grayscale datasets is used to compare DeepFake picture detection. To improve feature separability, a hybrid classification pipeline that included Principal Component Analysis (PCA) and Support Vector Machine (SVM) was used. The results showed that RGB-based models outperformed grayscale models in terms of accuracy (93.6%), highlighting the significance of color information in recognizing artificial pictures. The results demonstrate the critical role chromatic cues play in enhancing the generalization and resilience of DeepFake detection systems.