Abstract
Employing machine learning algorithms to produce synthetic media, known as deepfake technology, has garnered considerable interest in contemporary times owing to its capacity for both favorable and unfavorable implications. The paper thoroughly examines deepfake technology, encompassing its creation and identification methods and its legal, ethical, and societal ramifications. The article commences by presenting a comprehensive summary of the technology behind deepfake and its fundamental machine-learning algorithms. The subsequent discourse pertains to the basic metrics employed in assessing deepfake generation, the identification methodologies, and the prevalent benchmarks and datasets utilized for evaluating these algorithms. The study thoroughly examines deepfake technology, encompassing its methods of generation and detection, metrics for evaluation, datasets for benchmarking, and the challenges and constraints associated with its use. The review scrutinizes diverse techniques for generating deep fakes, encompassing Generative Adversarial Networks (GANs), autoencoders, and neural networks. Style transfer, alongside their corresponding metrics for evaluation, namely Peak Signalto- Noise Ratio (PSNR), Structural Similarity Index (SSIM), Fréchet Inception Distance (FID), and Inception Score (IS). The text delves into an analysis of deepfake detection techniques, encompassing image and video-based methodologies and the corresponding evaluation metrics. These metrics include accuracy, recall, F1 score, accuracy, AUC-ROC, and AUC-PR. The article additionally examines the benchmarks and datasets employed to evaluate the efficacy of deepfake detection algorithms. These include the Deepfake Detection Challenge (DFDC), the FaceForensics++, Celeb-DF, and DeeperForensics-1.0 datasets. This paper presents an overview of the challenges and limitations of generating and detecting deepfakes.
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