A review on Ethical and Legal Challenges of Deepfake Technology

Deepfake videos are becoming a significant social concern. These videos are created using artificial intelligence (AI) techniques, particularly deep learning, and they pose a growing challenge for society. Unscrupulous individuals exploit deepfake technology to disseminate false information, including fake images, videos, and audio clips. The rise of convincing fake content poses serious threats to politics, security, and personal privacy. Most methods for detecting deepfake videos rely heavily on data-driven approaches. This survey paper aims to provide a comprehensive analysis of both the generation and detection of deepfake videos. One of its key contributions is classifying the various challenges faced in detecting these deceptive videos. It delves into data-related issues, such as unbalanced datasets and insufficiently labeled training data. Training challenges are also highlighted, particularly the need for substantial computational resources. Additionally, the paper addresses reliability issues, including overconfidence in detection methods and the emergence of new manipulation techniques. The research underscores the prevalence of deep learning-based methods in deepfake detection, despite their computational demands and limitations in generalization. However, it also points out the drawbacks of these methods, such as their inefficiency and generalization challenges. Furthermore, the study critically assesses deepfake datasets, stressing the importance of high- quality datasets to enhance detection methods. It also identifies significant research gaps, paving the way for future investigations into deepfake detection, including the development of robust models for real-time detection.