Response to Reviewer 1iwK's Concerns (part 1)
Thank you for your timely response and active engagement in the discussion! Below is our latest response:
> ***Weakness #1: The details of Equation 3 and its relationship with appendix H of guided diffusion***
We have carefully read Appendix H of the guided diffusion paper [1] (titled "Conditional Diffusion Process") and would like to provide some clarifications regarding your comments:
- Appendix H of [1] provides an overview of the theory behind the **conditional diffusion model**. The conditional diffusion model can be represented by $q(x_{t}|x_{t+1}, y)$. Using Bayes' theorem, it can be further broken into two components: $q(x_{t}|x_{t+1})$ representing the unconditional diffusion model and $q(y|x_{t})$ representing conditional guidance. For more detailed insights, please refer to Equation 55~61 in Appendix H of [1] along with the subsequent analysis. It's important to emphasize that the theory of the conditional diffusion model discussed in Appendix H of [1] is $\textcolor{red}{\textbf{independent of specific sampling algorithms}}$, whether it's DDIM or DDPM, ODE or SDE.
- Our Equation 3 describes the general DDIM sampling process, which is applicable to both conditional and unconditional diffusion models. In summary, there is a **noticeable distinction** between Equation 3 and Appendix H of [1].
However, we also find your suggestion to provide more detailed description for Equation 3 constructive. We will incorporate your suggestion into the revised version.
> ***Weakness #3: Issues of accuracy on extraction and the setting of "guidance scale=1"***
- Regarding the issue of fidelity, or more specifically, the **accuracy on extraction**, our clarification is as follows:
- Firstly, we agree with your perspective that fidelity is a crucial aspect of image steganography algorithms. However, achieving higher PSNR solely **under ideal conditions** falls short of practical significance. In real-world scenarios, container images often carry complex real-world distortions. Investigating fidelity under such conditions is more practical and significant. However, $\textcolor{red}{\textbf{this point has been overlooked in previous works}}$, which is precisely where CRoSS holds a unique advantage. CRoSS demonstrates better fidelity under various distortion conditions, showcasing enhanced robustness.
- Additionally, we believe that pixel-wise evaluation metrics like PSNR can be misleading. A significant amount of research has concentrated on boosting PSNR **under ideal conditions**, disregarding real-world application needs. While our CRoSS may not achieve the same level of **objective fidelity** as compared to previous methods, its **subjective fidelity** in terms of visual perception is indeed satisfactory.
- Lastly, $\textcolor{red}{\textbf{there is substantial room for improvement in CRoSS's objective fidelity.}}$ There are two possible approaches: (1) Not using latent diffusion models like Stable Diffusion but employing image diffusion models such as Imagen [2]. (2) Introducing strategies that require training. It's worth noting that CRoSS currently operates **without requiring training**. However, by maintaining the overall framework and simultaneously incorporating dedicated training for image steganography tasks, it is possible to mitigate its objective fidelity disadvantage while preserving its advantages in robustness and security. This would be a valuable topic for the future research.
- Regarding the issue of background similarity and the setting of **"guidance scale=1"**, our clarification is as follows:
- Currently, in order to ensure subjective fidelity, we have adopted the Stable Diffusion with a guidance scale of 1. This has indeed led to certain issues such as background similarity. What we want to emphasize here is that these issues $\textcolor{red}{\textbf{do not stem from the CRoSS framework itself}}$, but rather from the specific choice of the diffusion model used.
- Because the editing capability of Stable Diffusion with a guidance scale of 1 is limited, we adopted experimental settings such as using similar backgrounds. However, the generative capability of diffusion models is **continuously evolving**. For instance, recent advancements like the upgraded version of Stable Diffusion (SDXL) are further pushing the boundaries of diffusion model capabilities. In the future, we hope to leverage **more powerful** classifier-free diffusion models with a guidance scale of 1, or even non-classifier-free diffusion models, to achieve **stronger editing capabilities**.
- This way, we can enhance the CRoSS framework in a **plug-and-play manner**. CRoSS, as the first effort to integrate diffusion models into image steganography, introduces a new framework, showcasing pioneering value.
> ***References***
[1] Diffusion Models Beat Gans on Image Synthesis. NeurIPS 2021.
[2] Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. NeurIPS 2022.