MapDec-R1: Cartographic Evidence-Grounded Reasoning for Map Deepfake Detection

AI-generated maps are becoming increasingly difficult to distinguish from authentic maps based on visual appearance alone. Unlike natural images, maps are structured representations governed by cartographic rules and spatial-semantic constraints, providing domain-specific authenticity evidence beyond the visual artifacts commonly exploited by general deepfake detectors. In this work, we propose MapDec-R1, a cartographic evidence-grounded reasoning framework for map deepfake detection. MapDec-R1 constructs a Cartographic Evidence Space that organizes authenticity cues across visual appearance, cartographic elements, and spatial-semantic relations, enabling detection based on map-specific evidence rather than generator-specific artifacts. We further introduce Cartographic Evidence-Grounded Reasoning Alignment, which aligns model reasoning with verifiable cartographic evidence instead of directly imitating teacher-generated reasoning trajectories. To jointly optimize detection and evidence-grounded reasoning, a two-stage curriculum reinforcement learning strategy first establishes reliable authenticity discrimination and then incorporates evidence-level feedback under outcome constraints. We also construct TMap-DD, a benchmark covering diverse map styles, real-map sources, and generation mechanisms. Extensive experiments demonstrate that MapDec-R1 achieves strong detection and cross-source generalization while producing interpretable authenticity reasoning grounded in cartographic evidence.