BASS :Benchmarking AI-Synthetic Song Detection under Evolving Generators and Audio Corruptions

Rapid updates to AI music generators challenge synthetic song detectors trained on fixed collections, while common audio processing can further degrade their performance. We introduce **BASS** (**B**enchmarking **A**I-**S**ynthetic **S**ong Detection under Evolving Generators and Real-World Corruptions), a benchmark containing 47,332 synthetic songs spanning 12 Suno and Mureka versions, together with rich generation metadata and 23 audio corruption conditions for robustness evaluation. We further propose **CARLA** (**C**orruption-**A**ware **R**eal-Density **L**earning and **A**daptation), a two-stage framework that learns a corruption-aware real-music density from human songs and adapts to each emerging generator through a lightweight LoRA adapter trained with at most 100 synthetic support songs. Experiments on BASS show that CARLA achieves state-of-the-art performance while remaining robust compared with baseline models. These results highlight the complementary importance of adapting to new generators and modeling robustness to audio corruption. Code and data are available at https://anonymous.4open.science/r/BASS-DAF5/.