Task Completed: Literature Review

During the first week of the project, an extensive literature review was conducted to understand the foundations of deepfake technology and the current state of deepfake detection methods. The review focused on two major aspects:

1. Deepfake Generation Techniques

Studied how Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and face-swapping algorithms are used to generate deepfake images.
Reviewed different models such as DeepFakes, Face2Face, and StyleGAN which have been widely used in synthetic image generation.
Identified the evolving nature of these techniques, making detection increasingly difficult.

2. Deepfake Detection Approaches

Analyzed existing detection techniques, especially those using traditional forensic tools like metadata and watermark analysis.
Found that these traditional methods are largely ineffective due to the high quality and realism of AI-generated content.
Focused on deep learning approaches, particularly Convolutional Neural Networks (CNNs), which analyze inconsistencies in textures, lighting, and facial symmetry.

Relevant research papers, conference publications, and existing datasets were referred to for this review. The literature review helped in identifying key limitations of current systems and laid a strong foundation for the model development phase.