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Home > Computing and Information Technology > Computer science > Digital signal processing (DSP) > Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before it is Created?(19 Foundations and Trends® in Engineering)
Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before it is Created?(19 Foundations and Trends® in Engineering)

Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before it is Created?(19 Foundations and Trends® in Engineering)


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About the Book

Generative adversarial networks (GANs) and diffusion models have dramatically advanced deepfake technology, and its threats to digital security, media integrity, and public trust have increased rapidly. This monograph explores zero-shot deepfake detection – an emerging method even when the models have never seen a particular deepfake variation. Topics covered in this monograph include self-supervised learning, transformer-based zero-shot classifier, generative model fingerprinting, and meta-learning techniques that better adapt to the ever-evolving deepfake threat. In addition, AI-driven prevention strategies that mitigated the underlying generation pipeline of the deepfakes before they occurred are suggested. They consisted of adversarial perturbations for creating deepfake generators, digital watermarking for content authenticity verification, real-time AI monitoring for content creation pipelines, and blockchain-based content verification frameworks. Despite these advancements, zero-shot detection and prevention faced critical challenges such as adversarial attacks, scalability constraints, ethical dilemmas, and the absence of standardized evaluation benchmarks. These limitations are addressed by discussing future research directions on explainable AI for deepfake detection, multimodal fusion based on image, audio, and text analysis, quantum AI for enhanced security, and federated learning for privacy-preserving deepfake detection. The monograph also highlights the important role of interdisciplinary collaboration between AI researchers, cybersecurity experts, and policymakers to create resilient defenses against the rising tide of deepfake attacks.

Table of Contents:
1. Introduction 2. Advances in Deepfake Generation and Their Implications 3. Zero-shot Deepfake Detection: Methods and Approaches 4. Zero-shot-based Prevention Strategies for Deepfake Generation 5. Challenges in Zero-shot Deepfake Detection and Prevention 6. Future Research Directions 7. Conclusion About the Authors References


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Product Details
  • ISBN-13: 9781638286165
  • Publisher: now publishers Inc
  • Publisher Imprint: now publishers Inc
  • Height: 234 mm
  • No of Pages: 172
  • Returnable: Y
  • Returnable: Y
  • Returnable: Y
  • Returnable: Y
  • Sub Title: Can AI Predict and Prevent Fake Content Before it is Created?
  • Width: 156 mm
  • ISBN-10: 1638286167
  • Publisher Date: 28 Aug 2025
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Returnable: Y
  • Returnable: Y
  • Returnable: Y
  • Series Title: 19 Foundations and Trends® in Engineering
  • Weight: 304 gr


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