With advances in rendering techniques and generative adversarial networks, computer-generated (CG) images tend to be indistinguishable from photographic (PG) images. Revisiting previous works towards CG image forensic, we observed that existing datasets are constructed years ago and limited in both quantity and diversity. Besides, current algorithms only consider the global visual features for forensic, ignoring finer differences between CG and PG images. To mitigate these problems, we first contribute a Large-Scale CG images Benchmark (LSCGB), and then propose a simple yet strong baseline model to address the forensic task. On the one hand, the introduced benchmark has three superior properties, 1) large-scale: the benchmark contains 71168 CG and 71168 PG images with the corresponding expert-annotated labels. It is orders of magnitude bigger than previous datasets. 2) high diversity we collect CG images from 4 different scenes generated by various rendering techniques. The PG images are varied in terms of image content, camera models, and photographer styles.