Book

Artificial intelligence is transforming how digital content is created, distributed, and understood. At the same time, AI-generated images, cloned voices, manipulated videos, and convincing deepfakes are making it increasingly difficult for journalists and audiences to distinguish authentic evidence from synthetic media. Artificial Intelligence and Digital Truth: Detecting Deepfakes in Journalism examines the technical, editorial, and ethical challenges that deepfakes create for modern journalism. It introduces a practical hybrid framework that combines lightweight visual detection, temporal analysis, frequency-based examination, audio verification, content provenance, and human editorial review. Rather than treating an automated detection score as a final judgment, the book explains how artificial intelligence can support a broader evidence-based verification process. The book explores how synthetic media is produced, why many detection systems struggle with compression and previously unseen manipulation methods, and how newsrooms can preserve digital evidence, assess sources, evaluate provenance information, and manage uncertainty. It also addresses model limitations, cross-dataset testing, privacy, governance, newsroom security, legal considerations, and the importance of maintaining human accountability. Designed for journalists, editors, newsroom technologists, AI researchers, media educators, students, and policymakers, this book provides both technical insight and practical guidance. It presents verification workflows, implementation roadmaps, editorial playbooks, documentation templates, and recommendations for building responsible newsroom systems.