AI Image Classifier

AI Image Classifier is a machine learning model that can effectively classify Real and AI Generated Images.

Table of Contents

• AI Image Classifier
• Table of Contents
• Brief Overview
• Demo
• Installation
• Data Sources
• Model
• Testing and Evaluation
• Deployment

Brief Overview

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This project involved building a machine learning model that can classify real and AI generated images. The first step was to create a dataset comprising of AI-generated and real images. To accomplish this, pygoogle_image was used, with which images from Google were downloaded. Apart from Google Images, two more datasets were used.

• CIFAKE: Real and AI-Generated Synthetic Images
• Ai Generated Images | Images Created using Ai from Kaggle

These images were preprocessed and features were extracted. Then, a Convolutional Nueral Network based Classifier model was constructed and trained on the dataset containing around 1,00,000 images.

Demo

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Here is a short demo of the deployed web application.

demo.mp4

Installation

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Open Git Bash and change the directory to the location where the repository is to be cloned. Then, type the following commands.

  git init

  git clone https://github.com/SanKolisetty/AI-Image-Classifier.git

Now, install the requirements using the following command.

   pip install -r requirements.txt 

To access or use the application, open a terminal in the cloned repository folder and run the following command.

  streamlit run deploy.py

Finally, browse the link provided in your browser.

Data Sources

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The dataset comprised of Real and AI Generated Images. Images were collected from Google using the python library pygoogle_image. Every single image was verified to avoid issues such as incorrect and ambigous images. Two other datsets were also used.

• CIFAKE: Real and AI-Generated Synthetic Images ( Link )
• Ai Generated Images | Images Created using AI ( Link )

Combing the three datasets, we have:

• Train Set: 101031
• Test Set: 20000

Model

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The CNN model was built similar to VGG16 and was trained on the train dataset. The model has a Convolutional Layer, MaxPooling Layer and a Dropout Layer repeated four times with increasing sizes of filters of the Convolutional Layer i.e. 32, 64, 128, and 256. It has 3 dense layers. The activation for all the layers is relu except for the last layer, which has activation sigmoid.

The model architecture and model summary are uploaded.

Testing and Evaluation

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On evaluation, model achieved an accuracy of 93.90%

Classification Report:

Confusion Matrix:

0 is Real and 1 is AI Generated

Deployment

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The application has been deployed in the Streamlit Cloud.

You can access it here: https://ai-image-classifier-sk.streamlit.app/