Self-Consistency-Based Fake Media Detection Using Multi-Perspective LLM Reasoning

The rapid proliferation of synthetic and misleading media has intensified the need for robust fake media detection systems. While large language models (LLMs) have recently been employed as classifiers for misinformation detection, most existing approaches treat them as black-box predictors, overlooking their internal reasoning dynamics. In this paper, we propose a novel framework for fake media detection based on self-consistency divergence across multi-perspective LLM reasoning. Instead of generating a single verdict, the proposed method prompts an LLM to analyze a given media item from multiple independent reasoning perspectives, including factual consistency, logical coherence, emotional manipulation, and source credibility. By sampling multiple reasoning chains under controlled stochasticity, semantic divergence and logical instability across the generated explanations are quantified. We hypothesize, and empirically show, that fake media induces significantly greater reasoning variance than genuine content because fabricated narratives often lack stable factual grounding. Experiments conducted on benchmark fake news datasets show that reasoning divergence serves as a strong discriminative signal, improving detection robustness and interpretability compared to standard single-pass LLM classifiers. The findings suggest that internal reasoning instability can function as an intrinsic reliability metric, opening a new direction for explainable and model-centric fake media detection.