 **Summary:**
The paper introduces a novel method for sanitizing images to remove hidden information using a diffusion model, termed DM-SUDS. This approach aims to improve image quality compared to existing methods like SUDS, demonstrating a significant improvement in metrics such as MSE, PSNR, and SSIM. The methodology is evaluated against a variety of steganography techniques including LSB, DDH, and UDH, showcasing its effectiveness in preserving image quality while sanitizing the image. However, the paper's contribution is questioned due to its simplicity and the lack of significant novelty, as it primarily involves the application of a diffusion model to an existing method without substantial modifications.

**Strengths:**
- The paper is well-written, clear, and easy to follow, making it accessible to a broad audience.
- The methodology is straightforward and easy to understand, with a focus on improving image quality through the application of a diffusion model.
- The paper includes a detailed analysis of the proposed method, which is backed by empirical results that demonstrate its effectiveness.
- The use of a diffusion model for sanitization is a novel approach, showing potential for improving image quality in steganography applications.

**Weaknesses:**
- The paper lacks a thorough discussion on the limitations of the proposed method, which is crucial for understanding its applicability and effectiveness in real-world scenarios.
- The novelty of the paper is questioned as the primary contribution appears to be the application of a diffusion model to an existing method, rather than a significant advancement in the field.
- The evaluation of the method is limited to a single dataset (CIFAR-10), which may not fully demonstrate the method's effectiveness across different datasets and steganography techniques.
- The paper does not adequately address the potential negative societal impacts of the work, which is a significant omission in ethical considerations.
- The paper does not compare its method with other existing sanitization methods, which could have provided a more comprehensive evaluation of the proposed approach.

**Questions:**
- Could the authors provide a more detailed discussion on the limitations of the proposed method and how these limitations affect its practical applicability?
- How does the proposed method compare to other existing sanitization methods, particularly in terms of effectiveness and efficiency?
- Can the authors clarify the specific contributions of the paper beyond the application of a diffusion model to an existing method?
- What are the potential negative societal impacts of the proposed method, and how are these addressed or mitigated by the authors?
- Could the authors expand the evaluation of the method to include a broader range of datasets and steganography techniques to better demonstrate its generalizability and effectiveness?

**Soundness:**
3 good

**Presentation:**
3 good

**Contribution:**
2 fair

**Rating:**
5 marginally below the acceptance threshold

**Paper Decision:**
- Decision: Reject
- Reasons: The paper, while introducing a novel method for sanitizing images using a diffusion model, lacks significant novelty and depth in its methodological contributions. The primary focus on image quality improvement does not sufficiently distinguish it from existing methods, and the lack of comprehensive evaluation across different datasets and steganography techniques limits its applicability and impact. Furthermore, the paper does not adequately address the ethical implications of its work, which is crucial in the context of steganography. These factors collectively contribute to the decision to reject the paper.