Image steganography is a technique for hiding secret data within a cover image and thus making the data immune to typical steganalysis attacks. Recently, there is a great concern about color distortion in most commonly used color-based image steganography methods. To address this issue, we first propose a steganography method that hides secret images in grayscale image and creates a color image. Specifically, we design a colorable information hiding mechanism to convert the image steganography task to gray-to-color conversion. In addition, we exploit the invertible neural network to realize image hiding and recovering in the forward and backward propagation, respectively. Meanwhile, we develop a perceptual loss function to effectively improve the quality of the stego image for increasing steganography security. Experiments indicate that the proposed method outperforms other state-of-the-art image steganography on secret image recovery, embedding capacity, and steganography security over different datasets.