arXiv:2606.00136v1 Announce Type: cross Abstract: The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffective. This survey synthesizes emerging research to demonstrate a paradigm shift toward the proactive detection of emerging inauthentic narratives. In this survey, we adopt a unified, lifecycle-based taxonomy to combine socio-technical lifecycle models of adversarial campaigns with advanced computational methodologies for emerging inauthentic narrative detection. By structuring the analysis around th
The rapid proliferation of sophisticated generative AI models necessitates immediate proactive strategies to counter the threat of adversarial synthetic content, as traditional reactive methods are proving ineffective.
This survey details advanced computational methodologies combined with socio-technical models, offering a framework for proactive detection of inauthentic narratives, crucial for maintaining information integrity and digital ecosystem resilience.
The focus in combating synthetic content shifts from reactive detection to proactive, lifecycle-based analysis, demanding new strategies for identifying and mitigating emerging inauthentic narratives.
- · Cybersecurity firms
- · AI ethics and safety researchers
- · Social media platforms prioritizing integrity
- · Intelligence agencies
- · Malicious actors using GenAI
- · Platforms with weak content moderation
- · Traditional reactive detection methods
- · Public trust in digital information
Increased investment and development in proactive AI-driven content authentication and detection systems across various sectors.
A potential arms race between generative AI capabilities for creating synthetic content and defensive AI for detecting it, leading to continuous evolution of both.
Enhanced global regulatory frameworks and international cooperation to combat misinformation and disinformation campaigns empowered by generative AI.
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