AI Art Authenticator: Deep Learning Image Classification

The AI Art Authenticator: Deep Learning Image Classification project leverages deep learning techniques to tackle the challenging task of distinguishing between images that are produced using artificial intelligence and those that are not. With a comprehensive dataset consisting of 10,000 images that are produced using artificial intelligence and those made by humans for training, and an additional 3000 images in each category for testing, this project achieves an accuracy rate of 87.95%. A CNN architecture was developed for the project's core component utilizing Tensor-Flow and Keras. This CNN has layers that include convolutional, pooling, and fully connected layers, and its final layer uses sigmoid activation for binary classification. When training the model, enhanced picture data is utilized to increase the model's robustness and generalizability. The project also includes a testing phase where a custom function processes images and predicts their authenticity. Notably, the confusion matrix reveals the model's effectiveness, cleanly separating photos produced by AI from those that are not. This AI Art Authenticator holds significant potential for applications such as content verification, copyright protection, and art authentication. It showcases the power of deep learning in distinguishing between human-generated and AI-produced content, contributing to the ongoing discourse around AI ethics and the authenticity of digital media.