Generative AI and Deep fake s: Ethical Implications and Detection Techniques

Abstract

Generative Artificial Intelligence (AI) has revolutionized content creation, enabling the synthesis of highly realistic images, videos, audio, and text. However, this advancement has also given rise to deep fake s—synthetic media that can convincingly mimic real individuals and events—posing significant ethical and societal challenges. This paper explores the dual-edged nature of generative AI by examining the ethical implications associated with deep fake s, including privacy violations, misinformation, manipulation, and the erosion of trust in digital content. Alongside these concerns, we provide a comprehensive overview of current detection techniques ranging from traditional digital forensic methods to state-of-the-art machine learning approaches. We highlight the strengths and limitations of existing solutions and discuss the ongoing arms race between deep fake generation and detection. Finally, we identify future research opportunities that focus on enhancing detection robustness, developing ethical frameworks, and fostering interdisciplinary collaboration. This paper aims to contribute to a balanced understanding of generative AI’s potential and risks, emphasizing the urgent need for ethical responsibility and technological innovation to safeguard information integrity in the digital age.