Federated Learning for Internet of Vehicles: IoV Image Processing, Vision & Intelligent Systems

Digital Deception: Uncovering the Dark Side of AI in Social Networks is a critical investigation into how artificial intelligence silently influences, manipulates, and, at times, undermines digital interactions across social platforms. Bridging disciplines such as computer science, sociology, and ethics, the book exposes how AI technologies contribute to misinformation, surveillance, identity manipulation, and psychological exploitation in the digital sphere.
With chapters on algorithmic bias, deep fakes, federated learning, and intrusion detection, the book reveals the hidden mechanisms that shape user behavior and societal discourse. It explores the ethical implications of AI-powered content curation, privacy violations, and the rise of automated cyberattacks—while proposing regulatory and technological countermeasures. Case studies and real-world examples illustrate the consequences of unchecked AI deployment and the erosion of trust in online spaces.
Key features:
Examines misinformation, digital surveillance, and algorithmic bias
Presents real-world case studies and AI behavior models
Highlights privacy concerns and ethical frameworks
Proposes AI-driven defenses and user empowerment strategies

Readership:
Researchers, students, technologists, policymakers, and activists seeking to understand and address the hidden risks posed by AI in our digitally connected world.
Digital Deception: Uncovering the Dark Side of AI in Social Networks is a critical investigation into how artificial intelligence silently influences, manipulates, and, at times, undermines digital interactions across social platforms. Bridging disciplines such as computer science, sociology, and ethics, the book exposes how AI technologies contribute to misinformation, surveillance, identity manipulation, and psychological exploitation in the digital sphere.
With chapters on algorithmic bias, deep fakes, federated learning, and intrusion detection, the book reveals the hidden mechanisms that shape user behavior and societal discourse. It explores the ethical implications of AI-powered content curation, privacy violations, and the rise of automated cyberattacks—while proposing regulatory and technological countermeasures. Case studies and real-world examples illustrate the consequences of unchecked AI deployment and the erosion of trust in online spaces.
Key features:
Examines misinformation, digital surveillance, and algorithmic bias
Presents real-world case studies and AI behavior models
Highlights privacy concerns and ethical frameworks
Proposes AI-driven defenses and user empowerment strategies

Readership:
Researchers, students, technologists, policymakers, and activists seeking to understand and address the hidden risks posed by AI in our digitally connected world.