EnhancingDeepFakeDetectionUsingTargeted Facial Region Analysis in Visual Data
RamakrishnanRaman, Yassir A. Farooqui, K Nisha, ManishaPaliwal · 2025
DeepFake technology, which leverages deep learning to create hyper-realistic synthetic media, has raised significant concerns regarding misinformation, privacy violations, and digital security. Despite advancements in detection techniques, existing methods face challenges in effectively identifying subtle and localized manipulations in facial features. This study proposes a novel DeepFake detection approach that focuses on targeted facial region analysis, emphasizing areas prone to manipulation such as the eyes, mouth, and jawline. By employing a convolutional neural network (CNN) integrated with an attention mechanism, the model assigns greater importance to these critical regions, enabling precise identification of artifacts often overlooked by traditional methods. The proposed system undergoes rigorous preprocessing to normalize inputs and extract key facial landmarks before feeding them into the feature extraction module. The attention layer dynamically adjusts weights to prioritize regions exhibiting inconsistencies, enhancing the reliability of predictions. Comprehensive experiments conducted on benchmark datasets, including Celeb-DF and DFDC, reveal that the model achieves superior detection performance with notable improvements in accuracy, precision, recall, and ROC-AUC metrics compared to existing techniques. Additionally, visualization of attention heatmaps demonstrates the model’s ability to effectively isolate manipulated areas, offering deeper insights into its decision-making process. The proposed approach provides a robust solution for combating the growing challenges posed by DeepFake media, with potential applications in content moderation and digital forensics.KeywordsDeepFakedetection,facialregionanalysis,convolutionalneuralnet work,attentionmechanism,visualdataintegrity.