Focusing on the issue that information-hidden carriers will leave traces of modification, and it is fundamentally difficult to resist statistical steganalysis algorithm detection, a new security steganography model based on Generative Adversarial Network (GAN) was proposed. In this scheme, the generator model G in GAN was utilized to generate the original carrier information with noise as the driver. Next, by using the ±1 embedding algorithm, the secret message was embedded into the generated carrier information to generate the secret information. Finally, the secret information and the real image sample were used as the input of discriminator D in the GAN for iterative optimization. At the same time, discriminative model S was used to detect whether the image has a steganography operation, and timely feedback to generate image quality features, G&D&S competed with each other in the iterative process, and the performance was continuously improved. The proposed strategy is different from the two schemes of Steganographic GAN (SGAN) and Secure Steganography based on GAN (SSGAN). The main feature is that the secret information and the real image sample are used as input for the discriminative model, and the discriminative network D is reconstructed, so that the network can better evaluate the performance of the generated images. Compared with SGAN and SSGAN, the proposed model reduces the detection accuracy of steganalysis by 13.1% and 6.4% respectively. Experimental results show that the new information hiding scheme guarantees the security of information hiding by generating more suitable carrier information and can effectively resist the detection of steganographic algorithms, it is significantly superior to the contrast schemes in terms of anti-steganography analysis and security indicators.