## 1 Introduction  
In order to ensure the security of transmitted information, people often use cryptographic methods to encrypt secret information, thus hiding the meaning of the information and making the information unreadable, but this practice cannot hide the existence of the information. Irregular ciphertext information is often more susceptible to the attacker's suspicion during the transmission process, which generates a new security problem [1] of how to secretly transmit information under the eavesdropping of the attacker. Information- hiding technology may be the answer to this problem. This technology hides secret information in multimedia carriers (e.g., images, text, etc.) and then saves or forwards it, which does not easily draw attention, and only the receiver can extract the secret information, thus realising the purpose of privacy data preservation and sharing [2]. Thanks to the extensive use of images in practice, image steganography has gradually developed to be a popular field of information hiding [3]. In image steganography, a cover/host image is used as a container for secret information, and the resulting image containing the secret information is called a stego/container image. Image steganography needs to successfully hide secret information while minimising the impact on the image quality to avoid significant impact on visual perception. Secondly, the capacity limitation of steganography is an important consideration, as people always chase to hide more secret information. In addition, steganography needs to be secure, i.e., the presence of secret information is not detected when an attacker uses a steganalysis tool. Traditional image steganography typically involves embedding secret information by modifying pixel values in the spatial domain [4; 5] or spectral components in the transform domain [6; 7; 8]. However, these methods often degrade the quality of the stego image and introduce noticeable changes to its statistical properties, making them susceptible to steganalysis detection. With advancements in deep learning technology, new approaches to image steganography have emerged. For instance, the SGAN steganographic network [9] utilizes a deep convolutional neural adversarial network (DCGAN) to generate cover images that better align with real distribution, enhancing visual consistency for embedded secrets. Some methods [10; 11; 12] leverage Adversarial Neural Networks to address the statistical alteration issue present in traditional image steganography. These advancements mark a fusion of image steganography with deep learning techniques, presenting innovative solutions to improve both security and visual perception. Encoding and decoding operations in encoder-decoder networks exhibit similarity to the hiding and reconstruction processes in information hiding. To leverage this similarity, [PERSON] et al. [13] proposed the SteGAN text steganography model, which autonomously learns the hiding and reconstruction processes of secret information, significantly enhancing steganographic capacity. [PERSON] et al. [14] improved SteGAN further, enhancing the realism of the stego image. However, both methods are limited to hiding binary sequence information. To enhance steganographic capacity further, [PERSON] et al. [15] proposed the Deep Steganography model, achieving the steganographic effect of hiding one image within another, and later improving the model to enable large-capacity image steganography--hiding two images in a single image [16]. Despite these advancements, these methods, with separate designs for the encoder and decoder, involve irreversible processes for hiding and reconstructing information, leading to potential image quality degradation and security vulnerabilities against steganalysis. This paper aims to integrate the strengths of image steganography based on encoder-decoder networks and generative adversarial networks, proposing the CHASE image steganography network to achieve high capacity, invisibility, and security.