<p>Deepfake technology has been widely adopted and has changed the face of digital media, offering unprecedented opportunities for creative expression and entertainment while simultaneously posing a significant threat to information integrity. As synthetic media becomes increasingly indistinguishable from authentic content, the risk of misinformation and digital forgery has grown, necessitating more sophisticated detection frameworks. Deep convolutional neural networks have revolutionized facial analysis, yet achieving a balance between high-precision classification and computational efficiency remains a significant challenge. We propose Attentive ResNet101, a specialized architecture that integrates a Convolutional Block Attention Module to refine feature localization and highlight anomalous facial artifacts. By strategically applying both channel and spatial attention mechanisms, the model prioritizes high-frequency details essential for deepfake detection. We conduct a rigorous comparative analysis against established benchmarks, including ResNet50, MobileNetV2, and EfficientNetB0. Our experimental results demonstrate that the Attentive ResNet101 outperforms all baseline models, achieving a superior accuracy of 96.53% and recall of 98.39%. While lightweight models such as MobileNetV2 prioritize computational efficiency, the proposed attentive framework provided the most robust sensitivity to complex facial features. These findings suggest that the integration of attention-based feature refinement is a vital step toward securing the digital media landscape against the evolving threat of synthetic content.</p>