The existing traditional image steganography methods often adopt the selection and mapping approaches. Among all the pixels of the cover image, only those which have the portability of incorporating the secret bits without noticeable distortion are chosen. This results to small integration capacity. In this paper, we propose a generic system of image steganography that uses the architecture of auto-encoding networks based on end to end trained deep Convolutional Neural Networks to ensure the process of concealment and extraction. The trained network includes two sub-networks, one for hiding used by the sender to encode a color image in another of the same size. The other for extraction, used by the recipient to retrieve the secret image from the received stego image. To validate our system, we carried out several tests on a range of challenging images dataset publicly available such as ImageNet, CIFAR10, LFW, PASCAL-VOC12. Results show that the proposed method is generic regardless the source of the images used and solves the problem of capacity at acceptable PSNR and SSIM values.