Interdisciplinary Image Staganalysis & Visual Communication Challenges

The digital age is facing a crisis of visual authenticity due to steganography, which conceals messages within images. A major global threat that enables cybercrime, data exfiltration, and the coordination of extremist activities. This "cat-and-mouse" game demands detection evolution.Traditional steganalysis (statistical methods, classical machine learning like Rich Models) is failing against highly adaptive modern steganographic schemes. While cutting-edge deep learning methods are powerful, they are often "black boxes," lacking the transparency and interpretability essential for forensic and legal admissibility.Interdisciplinary Image Steganalysis & Visual Communication Challenges introduces a necessary, holistic paradigm shift. It integrates state-of-the-art deep learning innovations with critical insights from law, media governance, and ethics.The book offers comprehensive, future-ready strategies for developing generalizable detection models, establishing rigorous and multifaceted benchmarking, and creating policy frameworks to balance security with civil liberties.

Autorentext

Asma'u Shehu (Lecturer, Sokoto State University) specializes in AI, Cybersecurity, and DigitalForensics, and a Ph.D. candidate.Abdulhakeem Abdulazeez (Lecturer, UDUS) holds a Ph.D. in Computer Networks. Research centers on 5G/beyond and machine learning.Maniru Malami Umar (PhD Researcher) focuses on wireless networks, 5G, and cybersecurity.