Explainable Deepfake Video Detection using Convolutional Neural Network and CapsuleNet

TL;DR

This paper proposes a deepfake detection method combining CNN, CapsuleNet, and LSTM, enhanced with Explainable AI to improve transparency and reliability in distinguishing deepfake videos from real ones.

Contribution

It introduces a novel deepfake detection approach integrating CNN, CapsuleNet, and LSTM, along with explainability features for better interpretability.

Findings

Effective differentiation between deepfake and real frames.

Enhanced model transparency through Explainable AI.

Potential for practical deployment in security applications.

Abstract

Deepfake technology, derived from deep learning, seamlessly inserts individuals into digital media, irrespective of their actual participation. Its foundation lies in machine learning and Artificial Intelligence (AI). Initially, deepfakes served research, industry, and entertainment. While the concept has existed for decades, recent advancements render deepfakes nearly indistinguishable from reality. Accessibility has soared, empowering even novices to create convincing deepfakes. However, this accessibility raises security concerns.The primary deepfake creation algorithm, GAN (Generative Adversarial Network), employs machine learning to craft realistic images or videos. Our objective is to utilize CNN (Convolutional Neural Network) and CapsuleNet with LSTM to differentiate between deepfake-generated frames and originals. Furthermore, we aim to elucidate our model's decision-making process through Explainable AI, fostering transparent human-AI relationships and offering practical examples for real-life scenarios.