Lightweight Stylistic Consistency Profiling: Robust Detection of LLM-Generated Textual Content for Multimedia Moderation

• URL: http://arxiv.org/abs/2605.05950v1
• Date: Thu, 07 May 2026 09:58:00 GMT
• Title: Lightweight Stylistic Consistency Profiling: Robust Detection of LLM-Generated Textual Content for Multimedia Moderation
• Abstract summary: Large Language Models (LLMs) in content creation has made distinguishing human-written textual content a critical task for multimedia moderation.<n>Existing detectors often rely on statistical cues or model-specifics, making them vulnerable to paraphrasing and adversarial manipulations.<n>We propose LiLi SCP, a novel lightweight stylistic consistency profiling method for robust detection of LLM-generated textual content.
• Score: 17.622520491018438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing prevalence of Large Language Models (LLMs) in content creation has made distinguishing human-written textual content from LLM-generated counterparts a critical task for multimedia moderation. Existing detectors often rely on statistical cues or model-specific heuristics, making them vulnerable to paraphrasing and adversarial manipulations, and consequently limiting their robustness and interpretability. In this work, we proposeLiSCP , a novel lightweight stylistic consistency profiling method for robust detection of LLM-generated textual content, focusing on feature stability under adversarial manipulation. Our approach constructs a consistency profile that combines discrete stylistic features with continuous semantic signals, leveraging stylistic stability across multimodal-guided paraphrased text variants. Experiments spanning real-world multimedia news and movie datasets and conventional text domains demonstrate that LiSCP achieves superior performance on in-domain detection and outperforms existing approaches by up to 11.79% in cross-domain settings. Additionally,it demonstrates notable robustness under adversarial scenarios, including adversarial attacks and hybrid human-AI settings.

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