AI-BASED MULTIMEDIA SECURITY IN COMBATING ADVERSARIAL ATTACKS, DEEPFAKES, AND ETHICAL CONCERNS

Abstract

The integration of artificial intelligence (AI) into multimedia systems has revolutionized both content creation and security, but it has also introduced sophisticated threats such as adversarial attacks and deepfake forgeries. This review provides a comprehensive analysis of AI-based multimedia security, focusing on adversarial attacks, deepfake generation, and the defense mechanisms developed to counter these threats. We explore how adversarial techniques exploit vulnerabilities in AI models, examine the role of Generative Adversarial Networks (GANs) in producing highly realistic deepfakes, and review state-of-the-art detection methods, including AI-driven forensics and robust model training. Additionally, we discuss the limitations of current defenses in terms of scalability, real-time detection, and adaptability to novel attack strategies. The review also addresses the ethical and privacy concerns posed by these emerging technologies, particularly in sensitive domains such as politics, law enforcement, and personal media. Finally, we propose future research directions, such as the development of quantum-based multimedia cryptosystems, explainable AI models, and AI-enhanced cryptography, to enhance multimedia security in an increasingly adversarial landscape. This work aims to provide a roadmap for improving the resilience of AI systems to evolving multimedia threats while balancing security with ethical considerations.