Deepfake Detection Using Custom Densenet

Deepfake detection has grown to be an increasingly important research area due to the potential harm that deepfakes can cause to individuals and society. In recent years, deep learning techniques have shown promising results for detecting deepfake images and videos. In this research, we provide a deep learning-based strategy using Custom DenseNet for deepfake image detection. This model used a big dataset of actual and deepfake photos to train a convolutional neural network (CNN) using the Custom DenseNet architecture. The Custom DenseNet architecture is a deep residual network that has shown excellent performance in various computer vision tasks. To categorize images as real or fake, the CNN is trained using a binary cross-entropy loss function. To evaluate the approach, we used several performance indicators including F1-score, recall, accuracy, and precision. We also contrasted our strategy with other cutting-edge deepfake detection techniques. Our test results demonstrate that our solution employing Custom DenseNet surpasses existing deepfake detection techniques in terms of accuracy, precision, recall, and F1-score. We achieved an accuracy of 97%, a precision of 95%, and an F1- score of 75%. These results demonstrate the effectiveness of our approach in detecting deepfake images. Overall, our study shows that deep learning techniques, specifically the Custom DenseNet architecture, can be highly effective in detecting deepfake images.