Leveraging Deep Learning Approaches for Deepfake Detection: A Review

TL;DR

This review paper discusses the advancements in deep learning-based methods for detecting deepfakes, emphasizing the importance of developing accurate, cost-effective, and generalizable models to combat the spread of realistic fake media.

Contribution

It provides a comprehensive overview of various deep learning techniques for deepfake detection and analyzes their effectiveness across different datasets.

Findings

Deep learning models show promise in detecting deepfakes with high accuracy.

Generalizability across datasets remains a key challenge.

Cost-effective models are being developed for practical deployment.

Abstract

Conspicuous progression in the field of machine learning and deep learning have led the jump of highly realistic fake media, these media oftentimes referred as deepfakes. Deepfakes are fabricated media which are generated by sophisticated AI that are at times very difficult to set apart from the real media. So far, this media can be uploaded to the various social media platforms, hence advertising it to the world got easy, calling for an efficacious countermeasure. Thus, one of the optimistic counter steps against deepfake would be deepfake detection. To undertake this threat, researchers in the past have created models to detect deepfakes based on ML/DL techniques like Convolutional Neural Networks. This paper aims to explore different methodologies with an intention to achieve a cost-effective model with a higher accuracy with different types of the datasets, which is to address the generalizability of the dataset.