Steganography, as an important technology of information hiding, has gradually become one of the hot research directions of domestic and foreign scholars. It can not only ensure the security of the data itself, but also ensure that the data can be transmitted securely. Information hiding is one of the important ways to ensure the security of information in the network, which can hide secret information into the carrier imperceptibly. Compared with the previous steganography model for hiding color images based on deep learning, the steganography model in this article could achieve steganographic images with higher visual quality and stronger security. The experimental results show that our proposed model is achievable and effective.

Moreover, it utilizes the adversarial training between the encoder-decoder network and the steganalysis model to improve the ability to resist the detection of steganalysis models based on deep learning. Therefore, this paper proposes a steganography model-HIGAN, which constructs the encoding network composed of residual blocks to hide the color secret image into another color image of the same size to output a lower distortion and higher visual quality steganographic image. The larger the capacity, the worse and more unnatural the visual quality of carrier images after embedded. In addition, another important factor that limits the security of steganography is capacity. The rapid improvement of the accuracy of steganalysis models has caused a huge threat to the security of steganography. In recent years, many scholars have applied various deep learning networks to the field of steganalysis to improve the accuracy of detection. Steganography is one of the important methods in the field of information hiding, which is the technique of hiding secret data within an ordinary file or message in order to avoid the detection of steganalysis models and human eyes.

Image steganography tools