Steganographic algorithms are mainly evaluated by their security. Traditional steganographic frameworks use different embedding algorithms to achieve this goal. This means, embedding the secret message directly to a cover image. Nonetheless, with the development of sophisticated machine learning based steganalysis algorithms even the slightest modifications can be detected. This has triggered a lot of researchers to pursue cover-less steganography. Image synthesis with generative adversarial networks is one of the proposed solutions. However it has suffered drawbacks in convergence speed, training stability and realistic image generation. This research is about improving the generative steganography framework proposed in (Hu et al., 2018) using a more powerful generative adversarial network called “Boundary equilibrium generative adversarial network” as a generator, with the main goal of improving the quality of the generated stego-images without compromising the security. In addition to this, the recovery accuracy of the proposed Steganography without embedding scheme is increased by incorporating a Reed-Solomon error correction mechanism. Experimental results show that all of this goals have been achieved. The images generated are more realistic and better represent the real dataset having an inception score of 5.62 over celebA dataset, and on average an 8.2% increase in recovery accuracy when compared to (Hu et al., 2018).