This project received an Excellent grade for its attempt to utilize different ML models and clear presentation.

Project Structure

• Presentation.pdf: Presentation slides

• Report.pdf: Final project report

• Coding.twbx: Coding file

Project Title:

Image Authenticity Verification

Objective:

In this project, we attempt to distinguish between real dog images and AI-generated dog images using a CNN model and a CLIP model. Both models are trained using a supervised machine learning approach. We then compare the performance of both models to see how effectively these supervised models perform in classifying the images.

Key Findings:

The results show that CLIP comprehensively outperforms the traditional CNN across all evaluation metrics. Most notably, in detecting the rarer "AI Dog" category, CLIP delivers a major leap in stability and detection capabilities that traditional convolutional networks cannot achieve.

Tech Stack:

Python(Sckit-learn,Numpy,Tqdm,Matlibplot,Seaborn)

Data Visualization:

For more comprehensive analysis and detailed visualizations, please refer to the report or the coding file.