"Deepfake-detection" with Explainable AI

This repo contains the code, results, models for Deep Fake detection with XAI. Course Project for ECE 792 - Advanced Machine Learning

• To classify real and fake images, pretrained XceptionNet model is implemented.
• For model interpretability, algorithms such as LIME and Grad-CAM are implemented.

Datasets used:

• FaceForensic++: The dataset is used for benchmarking face manipulation detection. The fake videos are crated by four different manipulation methods. The dataset consists of a total of 1,000 videos (500 real and 500 manipulated) with 50 videos for each manipulation method. The data set can be found here
• Celeb-DF: The Celeb-DF dataset is a large-scale video dataset that is specifically designed for deepfake detection research. It is composed of over 590,000 video clips featuring 1,100 paid actors in a variety of facial expressions, head poses, and lighting conditions. The actors are celebrities from various domains such as politics, entertainment, and sports. The dataset can be found here

Project Description

• The objective of this project is to incorporate explainable AI (XAI) techniques to gain insights into the interpretability of the model. The project utilizes state-of-the-art XceptionNet to detect deepfakes, and then employs LIME and GradCam algorithms to visualize and analyze how the model interprets the results.
• Images were used instead of Videos from the dataset and a python script was written to extract images from the videos.

Files Description

• models: contains trained models for FaceForensic++ and Celeb-DF datasets
• outputs: contains output results of prediction of real and fake, lime and grad-cam algorithms
• plots: contains plots of accuracy, loss against epochs
• CelebDF_XceptionNet_Code.ipynb: contains code of xception on celebdf dataset
• FaceForensics_XceptionNet_Code.ipynb: contains code of xception on faceforensics dataset
• face_extraction_code_from_vidoes.py: contains code for extraction of faces from the videos
• grad_cam.ipynb: contains the code for gradCAM algorithm
• lime_code.ipynb: contains the code for LIME algorithms
• plot_curves.ipynb: contains the code to plot the graphs
• test_predictions.ipynb: continas the code for predicicion on test dataset