The rise of AI deepfakes following the launch of ChatGPT and its AI counterpart DALL-E has sparked fear that the boundary between real and fake can no longer be identified. In this study, it was found that machine learning algorithms can be reliably used to distinguish between real and AI-generated images of human faces when provided with high resolution 300x300-pixel images with an accuracy score of 99.07%. This paper will cover the findings of this study by reviewing the main ideas behind machine learning, supervised learning, and neural networks and then examining the application of these techniques to a binary classification problem involving image classification.