To address these limitations, this paper proposes a forensic-first hybrid deepfake face detection framework that integrates handcrafted local forensic descriptors with multi-CNN deep semantic representations. Specifically, manipulation-sensitive regions are captured using a Bag-of-Visual-Words (BoVW) model constructed from Histogram of Oriented Gradients (HOG) features extracted at salient keypoints detected via SURF, FAST, and BRISK. In parallel, high-level features are obtained from fine-tuned ResNet-50, MobileNet, and ShuffleNet models and fused at the feature level to capture complementary semantic information.