Below, we will introduce the details of each stage separately. In practical applications of image steganography, it is common to hide a single subject in an image, and this is also a problem that our method excels at solving. We employed two methods to obtain "Prompt1" and "Prompt2": an ChatGPT to generate the modified "Prompt2". The specific process of generating "Prompt2" is shown in Fig. A.1. We present examples from the Stego260 dataset in Fig. A.2, where each example consists of an image We show images from three categories: humans, animals, and general objects.

This supplementary content is mainly organized in the order of being referenced in the main manuscript. The architectures of the R networks are shown in Table 3. The training curve is shown in Figure 1. B.1 Where is the secret image encoded? Is every channel equally important?

Data hiding with deep neural networks (DNNs) has experienced impressive successes in recent years. A prevailing scheme is to train an autoencoder, consisting of an encoding network to embed (or transform) secret messages in (or into) a carrier, and a decoding network to extract the hidden messages. This scheme may suffer from several limitations regarding practicability, security, and embedding capacity. In this work, we describe a different computational framework to hide images in deep probabilistic models. Specifically, we use a DNN to model the probability density of cover images, and hide a secret image in one particular location of the learned distribution.

In this study, we attempt to place a full size color image within another image of the same size.

Image hiding is often referred to as steganography, which aims to hide a secret image in a cover image of the same resolution. Many steganography models are based on genera-tive adversarial networks (GANs) and variational autoencoders (VAEs). However, most existing models suffer from mode collapse. Mode collapse will lead to an information imbalance between the cover and secret images in the stego image and further affect the subsequent extraction. To address these challenges, this paper proposes StegOT, an autoencoder-based steganography model incorporating optimal transport theory. We designed the multiple channel optimal transport (MCOT) module to transform the feature distribution, which exhibits multiple peaks, into a single peak to achieve the trade-off of information. Experiments demonstrate that we not only achieve a trade-off between the cover and secret images but also enhance the quality of both the stego and recovery images. The source code will be released on https://github.com/Rss1124/StegOT.

Below, we will introduce the details of each stage separately. In practical applications of image steganography, it is common to hide a single subject in an image, and this is also a problem that our method excels at solving. We employed two methods to obtain "Prompt1" and "Prompt2": an ChatGPT to generate the modified "Prompt2". The specific process of generating "Prompt2" is shown in Fig. A.1. We present examples from the Stego260 dataset in Fig. A.2, where each example consists of an image We show images from three categories: humans, animals, and general objects.