Deepfake Technology: A Comprehensive Review of Trends, Applications, Ethical Concerns, and Challenges

Abstract

The digital age has fundamentally transformed how information is created and disseminated, raising critical concerns about the authenticity and trustworthiness of online content. Recent advances in Artificial Intelligence (AI), particularly deep learning, have given rise to deepfakes: highly realistic synthetic media generated by manipulating or replacing faces, voices, and actions in videos. While deepfake technology offers innovative applications across various industries, its rapid proliferation has also enabled malicious uses, including fake news, financial fraud, identity theft, and cyberattacks. Consequently, robust deepfake detection has become essential to preserving digital integrity and mitigating social and security risks. This paper presents a comprehensive review of deepfake technology, examining its creation techniques (e.g., autoencoders, generative adversarial networks), diverse media types (text, image, audio, video), and evolving detection methods. It also surveys publicly available datasets and evaluates the performance of state-of-the-art detection models. Beyond technical aspects, the review critically discusses the ethical, legal, and societal implications of deepfakes, including privacy violations, consent, misinformation, and regulatory challenges. By synthesizing current trends and identifying research gaps, this study aims to provide a balanced understanding of both the potential benefits and threats posed by deepfakes, and to inform future efforts in detection, governance, and responsible use.