Due to the rise in face manipulation algorithms, it is difficult to distinguish real videos from computer-generated videos. For this, the paper proposes HABSNet, a model that performs artifact localization and temporal consistency analysis. The facial regions identified by MTCNN are analysed by ResNeXt-50 backbone model with the help of two-stream attention mechanism for detecting blending and frequency artifacts, as well as a BiLSTM based inter-frame self-attention gate for detecting unnatural eye movements and head pose changes. Trained on the FaceForensics++ (C23) dataset, the HABSNet achieves an accuracy of 94.88% and generalizes well on Celeb-DF v2 (0.950) and DFDC (0.920). For multimedia security the proposed framework contributes by allowing identification of manipulated videos across varied deepfake generation techniques.