SHIELD: System for Harmful Explicit-Content Identification and Evaluation Through LLM-Driven Approach

Abstract

The surge in access to explicit content across various platforms has sparked major concerns, yet existing content filtering systems find it difficult to analyze different media formats leading to the spread of unchecked dissemination of harmful content. To tackle these shortcomings, the authors proposed SHIELD, which is an optimized end-to-end pipeline to detect & analyze explicit content, using a large-language-model (LLM) driven approach. SHIELD processes multi media inputs by segregating and preprocessing them, followed by converting all formats into text through advanced models, extracting meaningful textual context and subjecting the resulting data to two parallel evaluation mechanisms: an LLM-based classifier for contextual analysis, and a semantic vector-based scoring system for quantitative measurement. Explicitness classifications are output in a JSON format, which allows easy integration into real-world systems. When benchmarked against a manually curated ground truth dataset, the LLM-based system surpasses vector-based approach, with an accuracy of 93.32%, as against 67.81%. The pipeline shows robustness across all media types and file sizes, confirming its viability as a scalable, context-aware solution.