Leveraging Deep Learning Approaches for Deepfake Detection: A Review
It addresses the problem of detecting AI-generated fake media for social media platforms and security, but it is incremental as it reviews and builds on prior work.
This paper reviews existing deep learning methods for detecting deepfakes, aiming to develop a cost-effective model with higher accuracy across diverse datasets to improve generalizability.
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.