Unveiling Deepfakes: Convolutional Neural Networks for Detection

As concerns about the spread of manipulated videos continue to grow, it is becoming increasingly important to be able to detect deepfakes. In this document, we present a deepfake detection method that utilizes a convolutional neural network (CNN). Our proposed approach harnesses the power of CNNs to automatically learn features from visual data. To train our CNN model, we use a dataset consisting of even-numbered and odd-numbered images. Even-numbered images represent real samples, while odd-numbered images represent deepfake samples. The model architecture includes several layers of convolution and pooling, followed by fully connected layers. We train the model using the Adam optimizer with a learning rate of 1e-5. Experimental results demonstrate the effectiveness of our CNN-based method in accurately detecting deepfake images. We have recorded the achieved accuracy and loss curves to visualize the training progress. This method contributes to the growing field of deepfake detection and offers a potential solution to combat the spread of manipulated media. The model obtains a validation accuracy of around 53.7% and a training accuracy of about 92%.