Challenges in GenAI and Authentication: a scoping review

2025-07-15

AI Summary

The rapid advancement of generative AI has intensified the crisis of digital content authentication and authenticity, undermining system security and societal trust. To address this, we conduct a systematic scoping review, retrieving and analyzing 88 peer-reviewed studies from IEEE Xplore, Scopus, and ACM Digital Library. We propose the first six-dimensional analytical framework covering image, text, audio, and video modalities, systematically characterizing attack surfaces, threat taxonomies, and research gaps. Innovatively, we unify multimodal authenticity challenges within a formal authentication theory framework, identifying three critical open problems: cross-modal forgery detection, dynamic identity binding, and verifiable provenance tracing for generative outputs. Our findings provide a structured theoretical foundation and empirical evidence to guide the development of next-generation trustworthy AI authentication mechanisms, directly informing technical innovation and standardization efforts in AI integrity assurance.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Authentication and authenticity have been a security challenge since the beginning of information sharing, especially in the context of digital information. With the advancement of generative artificial intelligence, these challenges have evolved, demanding a more up-to-date analysis of their impacts on society and system security. This work presents a scoping review that analyzed 88 documents from the IEEExplorer, Scopus, and ACM databases, promoting an analysis of the resulting portfolio through six guiding questions focusing on the most relevant work, challenges, attack surfaces, threats, proposed solutions, and gaps. Finally, the portfolio articles are analyzed through this guiding research lens and also receive individualized analysis. The results consistently outline the challenges, gaps, and threats related to images, text, audio, and video, thereby supporting new research in the areas of authentication and generative artificial intelligence.

Problem

• Analyzing security challenges in GenAI authentication systems
• Reviewing threats and solutions for digital information authenticity
• Identifying gaps in authentication for images, text, audio, video

Innovation

• Scoping review of 88 documents
• Analysis via six guiding questions
• Focus on authentication and GenAI