A Study on Human-Computer Interaction in Deepfake Detection Systems

Human-Computer Interaction (HCI) plays a crucial role in the design of intelligent systems that humans can understand, trust, and effectively use. With the rapid advancement of artificial intelligence, deepfakes have emerged as a significant challenge to digital trust. Deepfakes are synthetic media generated using machine learning techniques such as Generative Adversarial Networks (GANs), capable of producing highly realistic manipulated images, videos, and audio. Although existing deepfake detection systems achieve high ac- curacy under controlled conditions, many prioritize algorithmic performance while neglecting usability, transparency, and human trust. This paper explores the integration of HCI principles into deepfake detection systems. We review existing detection methodologies, analyze cognitive load and explainable artificial intelligence (XAI), and examine ethical implications associated with synthetic media. A user-centered framework is proposed to enhance system usability and trust without compromising detection accuracy. The results highlight the importance of explainability, minimal cognitive load, and ethical interface design in improving real-world adoption of deepfake detection tools.