Adaptive image steganography has been becoming a hot topic, as it conceals covert information within the texture region of an image by employing a defined distortion function, which guarantees remarkable security. In spatial gray-scale image steganography, the research on designing steganographic distortion functions using generative adversarial networks has achieved a significant breakthrough recently. However, related studies of spatial color image steganography have not been reported yet so far. Compared with the gray-scale image steganography, color image steganography should preserve the RGB channel correlation and reasonably assign the embedding capacity among RGB channels simultaneously. This study first proposes a framework based on a generative adversarial network to automatically learn to generate the steganographic distortion function for spatial color images, which is termed CIS-GAN (color image steganography based on generative adversarial network). The generator is composed of two U-Net subnetworks. One of them generates the modification probability matrix, while the other adjusts the positive/negative modification probability to effectively weaken the damage to the RGB channel correlation. The analyzer of gray-scale image steganography is modified as an adversarial part of the network for color images. In addition, the generator can automatically learn to allocate the embedding capacity for the three channels via controlling the total steganographic capacity in the generator’s loss function. The experimental results show that the proposed framework outperforms the advanced steganographic schemes for spatial color images in resisting color image steganalysis.