This repository contains the dataset instructions, rubric, and reference materials necessary to complete the case study on AI vs. Real image classification.
The attached PDFs include both the Hook Document, which introduces the case study and your mission, and the Rubric, which outlines all requirements and criteria you must meet to complete the assignment successfully.
The dataset used for this case study is sourced from Hugging Face and can be loaded directly in Python using the following code:
from datasets import load_dataset
ds = load_dataset("Hemg/AI-Generated-vs-Real-Images-Datasets")No manual download is required. Make sure you have the datasets package installed before running this. You can install it by running:
pip install datasetsThe Reference Materials folder contains helpful resources related to AI-generated imagery, an introduction to Convolutional Neural Networks, and technical documentation to support your implementation. These are a starting point. You are encouraged to seek out additional sources as needed.
Dataset: Hugging Face — AI-Generated vs Real Images
https://huggingface.co/datasets/Hemg/AI-Generated-vs-Real-Images-Datasets
Image on Hook Document: C. Bush, "How to Spot AI-Generated Photos: Real vs. Fake," Charles Bush Photography, Nov. 3, 2025. [Online]. Available: https://charlesbushphoto.com/charles-bush-photography-news/2025/11/ai-generated-vs-real-photos.