The proliferation of AI-generated images has raised the need for reliable attribution methods capable of identifying not only whether an image is synthetic, but also which generative model produced it. This paper explores whether standard CNN architectures without model-specific adaptations can address the multiclass attribution task using only image-level content. We propose a benchmark including four popular neural architectures (ResNet-50, InceptionV3, DenseNet121, EfficientNetB0), tested on AGIQA-3K, a challenging dataset originally designed for image quality assessment. Two experimental setups are considered: a fine-grained 10-class configuration and a simplified 6-class version. We also examine how perceived quality affects attribution, showing that stylistic consistency plays a key role. Our results establish a simple, scalable benchmark to support future forensic research in AI-generated image attribution.