“Multimodal Surveillance Frameworks for Narcotics Detection on Social Media: A Review”

Abstract: The rapid growth of social media and encrypted messaging platforms has created new challenges for detecting online drug trafficking. Existing monitoring systems struggle with multimedia content, evolving slang, and hidden identities. This review surveys recent AI-based approaches, including multimodal detection, NLP pipelines, computer vision, blockchain forensics, and OSINT frameworks. Key limitations such as static vocabularies, high computational latency, and barriers posed by end-to-end encryption are highlighted. By synthesizing current methodologies and research gaps, this paper provides a comprehensive overview of the state of the art and outlines directions for future development in cyber-enabled narcotics detection.

Keywords: Artificial Intelligence in Cybersecurity, Multimodal Detection Frameworks, Drug Trafficking on Social Media, Encrypted Messaging Platforms (E2EE), Dynamic Slang and Identity Attribution.