The rapid growth of deep learning has significantly changed the way digital content is created and consumed. One of the most notable developments in this area is the emergence of deepfakes, which are artificially generated images or videos in which a person’s appearance is realistically altered[9]. Although such technology has useful applications in areas like entertainment, virtual reality, and education, its misuse has raised serious concerns related to misinformation, identity theft, and digital security. In recent years, detecting deepfakes has become an important research problem, as traditional methods often fail to identify highly realistic manipulated content. This study focuses on the use of Convolutional Neural Networks (CNNs) for detecting deepfake media. CNNs are particularly effective in analyzing visual data because they can automatically learn important spatial features from images without requiring manual feature extraction[2]. The proposed approach involves several stages, including data preprocessing, feature extraction, and classification. Initially, video data is converted into frames, and facial regions are extracted to focus on relevant information. These images are then normalized and resized to ensure consistency before being passed into the CNN model. The network is trained to distinguish between real and fake images by identifying subtle irregularities introduced during deepfake generation. To evaluate the effectiveness of the model, standard performance metrics such as accuracy, precision, recall, and F1-score are used. The results indicate that the CNN-based approach is capable of achieving high accuracy while maintaining balanced performance across different evaluation parameters. This suggests that deep learning models can play a crucial role in addressing the challenges posed by synthetic media. Therefore, future work may focus on improving model generalization and integrating multimodal approaches for better detection. Overall, this study highlights the potential of CNN-based systems in identifying deepfake content and contributes to ongoing efforts in ensuring digital media authenticity.

This study presented a deep learning-based approach for detecting deepfake media using Convolutional Neural Networks. The proposed system focuses on extracting meaningful facial features and analyzing them to distinguish between real and manipulated content. The experimental results demonstrate that the model achieves high accuracy and maintains a good balance between precision and recall. This indicates that CNN-based approaches are effective for identifying deepfake media[2]. At the same time, the study also highlights certain limitations, particularly in terms of generalization and sensitivity to data variations. These challenges suggest that further improvements are necessary to develop more robust detection systems. Overall, this research contributes to the growing field of deepfake detection and emphasizes the importance of developing reliable methods to ensure the authenticity of digital media.

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