How Do MLLMs Detect AI-Generated Facial Images? A Study of Cross-Source Detection, Provenance, and Explanations

Multimodal large language models (MLLMs) are increasingly used to detect AI-generated facial images and provide natural-language explanations. However, it remains unclear which signals influence these judgments and whether the visual evidence reported is reliable across repeated judgments. In this paper, we construct a 4,000-image benchmark covering AI-generated facial images from seven generation sources and real photographs, then evaluate five leading MLLMs in a cross-source setting. We first use full cross-source evaluation and fixed-effects analysis to examine whether detectors more readily identify images produced by models from the same developer. Provenance effects are further investigated by removing C2PA records and applying MarkNull for SynthID removal, while measuring the pixel-level changes. Finally, we assess judgment repeatability and the image support of model explanations. Across the evaluated systems, we find no general detection advantage for same-developer pairs, although effects vary across model combinations. Our provenance interventions further show that C2PA removal does not significantly change overall judgments, whereas SynthID removal produces detector- and source-dependent changes in detection behaviors. Notably, Gemini's detection rate decreases for Gemini Image outputs but increases for GPT Image 2 outputs. For explanations from correct detections, the corresponding images support most reviewed observations, although classification outcomes vary across repeated judgments. Our findings provide a clearer basis for evaluating cross-source MLLM detection and interpreting the visual evidence reported in model explanations.