Title: LD-RoViS: Training-free Robust Video Steganography for Deterministic Latent Diffusion Model
Venue: NeurIPS 2025
Accepted: yes
Keywords: Video steganography

latent diffusion model
Abstract: Existing video steganography methods primarily embed secret information by modifying video content in the spatial or compressed domains. However, such methods are prone to distortion drift and are easily detected by steganalysis. Generative steganography, which avoids direct modification of the cover data, offers a promising alternative. Despite recent advances, most generative steganography studies focus on images and are difficult to extend to videos because of compression-induced distortions and the unique architecture of video generation models. To address these challenges, we propose LD-RoViS, a training-free and robust video steganography framework for the deterministic latent diffusion model. By modulating implicit conditional parameters during the diffusion process, LD-RoViS constructs a dedicated steganographic channel. Additionally, we introduce a novel multi-mask mechanism to mitigate errors caused by video compression and post-processing. The experimental results demonstrate that LD-RoViS can embed approximately 12,000 bits of data into a 5-second video with an extraction accuracy exceeding 99\%. Our implementation is available at https://github.com/xiangkun1999/LD-RoViS.


Peer reviews:
comments: SUMMARY:
This paper proposes a generative steganography method utilizing pre-trained video diffusion models. It demonstrates high robustness against information loss during transmission by employing a classifier-free guidance trick and an embedding selection strategy called the Multi-mask mechanism.

STRENGTHS AND WEAKNESSES:
[Strength]

- The novel multi-mask mechanism effectively addresses robustness issues commonly encountered in diffusion-based steganography methods, particularly those caused by encoder-decoder errors in latent-based diffusion models.

- The writing and figures in the paper are sufficiently clear to understand the proposed method.

[Weakness]

- The number of hidden messages is significantly reduced due to masking, particularly with M_2. It appears that using random noise instead of X_0^1 and X_0^2, as done in StegaDDPM, may alleviate this issue.

- It would be helpful to quantify the computational overhead compared to standard text-to-video generation using metrics like latency or GFLOPS.

- The proposed method is only validated on the T2V-1.3B model of WAN2.1. Readers may wonder whether it would also work on other widely-used open-source models such as LTX-Video [A].

[A] Lightricks, https://huggingface.co/Lightricks/LTX-Video

QUESTIONS:
- In this method, can sender and receiver use M_2 = Mask( X_0, X_0^1), thereby avoiding the need to share k_2? Is there a specific reason for requiring k_2?

- Since the multi-mask mechanism enhances robustness, is it possible to increase capacity by embedding multi-bit messages using multiple values of k?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

comments: SUMMARY:
This paper introduces LD-RoViS, a training-free and compression-resilient video steganography framework that embeds secret information into latent variables by modulating the final timestep of a deterministic diffusion process.

STRENGTHS AND WEAKNESSES:
Strengths
- The proposed multi-mask mechanism selectively identifies stable regions in latent space, leading to strong resilience against video compression artifacts, which are prevalent in social media platforms.
- By operating at the final timestep, the method maintains determinism and enables precise control over where and how secret content is embedded in the latent space.

Weaknesses
- The method relies heavily on deterministic reverse sampling, restricting its use to a narrow subset of diffusion models. As the community shifts toward stochastic samplers or improved generative backbones (e.g., DiT), the method’s relevance may diminish without adaptation.
- Despite focusing on robustness, the method does not always guarantee full secret message reconstruction, even under clean conditions. This raises concerns about its practical reliability in high-stakes environments.

QUESTIONS:
To address concerns regarding generalizability and reliability, 
- testing transferability to alternative generative backbones
- analyzing reconstruction failures to understand the inherent brittleness of the encoding scheme

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a video steganography framework named LD-RoViS. It targets latent diffusion models that employ deterministic sampling strategies by modulating the CFG scale at the final step of the diffusion process, thereby constructing a steganographic channel for information embedding. The authors introduce a multi-masking mechanism to identify locations in the latent space that are both robust to distortion and highly discriminative for the embedded information. Experimental results demonstrate that the method achieves high embedding capacity and extraction accuracy.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThe paper extends the applicability of generative steganography to deterministic diffusion models. Currently, many generative steganography techniques rely on interfering with stochastic sampling processes, which prevents their direct application to increasingly popular deterministic samplers. This paper addresses this by utilizing the CFG scale, a universal parameter, to construct the steganographic channel.\n\nThe multi-masking mechanism proposed in the paper is a noteworthy component. This mechanism constructs an \"invariance mask\" by explicitly simulating the encoding-decoding and noise injection processes, and combines it with a \"discriminability mask\" generated by CFG modulation. Its effectiveness is supported by data in the Ablation Studies, demonstrating the necessity of both mask components for achieving high extraction accuracy.\n\nWeaknesses:\n\nThe paper lacks sufficient ablation studies regarding the design choices for the core CFG modulation strategy (e.g., why modulation occurs in the final step, why symmetric perturbation is used).\n\nPerformance evaluation is based on **only 100 video samples.** For a method aiming to handle diverse video content, this scale needs further improvement.\n\nThe experimental evidence presented in the paper originates from **a single model only**.\n\nSimilar to all generative methods, the upper bound of the paper's method performance (including visual quality and imperceptibility) is closely coupled with the performance of the chosen base video generation model; any flaws in the base model will directly transfer to the steganographic video.\n\nQUESTIONS:\nA core claim of the paper is that its method is applicable to general deterministic diffusion models, but all experiments are based on a single model employing a Flow Matching sampling strategy. We would like to know if this method remains effective on other types of deterministic samplers (e.g., deterministic DDIM)? Given the differences in update dynamics across various samplers, we hope the authors can provide additional arguments or experimental evidence to support this generalization claim. For example, a brief theoretical analysis or preliminary experiments on a publicly available DDIM-based model would greatly enhance our confidence in the method's scope of applicability.\n\nTo resist compression distortion, the multi-masking mechanism tends to embed information in texture-rich, high-frequency regions. However, these regions are typically also areas closely monitored by steganalysis tools. We would like to discuss with the authors whether this strategy of selecting embedding locations for the sake of robustness might inadvertently concentrate modification traces in specific areas, thereby forming new statistical features that could potentially be exploited by future, more advanced analysis models?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes a generative video steganography method for latent diffusion models by proposing an implicit parameter modulation strategy with keys and a multi-mask mechanism for identifying robust regions. It was demonstrated that this work can embed 12,000 bits of secret messages into a 5-second video with an accuracy >99%.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1) This work addresses an interesting problem of generative video steganography for modern video generation models based on DDIM. \n2) Evaluating for video compression seems practical.\n\nWeaknesses:\n1) Even though this work claimed that it is for video, theoretically the method for LDM for image generation can be applicable to LDM for video generation by replacing the decoder. Thus, in terms of novelty, it is unclear if this work has specific novelty over other generative image steganography methods with LDM. See [33], [36] as well as the followings:\n- Improved Generative Steganography Based on Diffusion Model (10.1109/TCSVT.2025.3539832)\n- Conditional Diffusion Model for Image Steganography (10.1109/AIPMV62663.2024.10692262)\n- Establishing Robust Generative Image Steganography via Popular Stable Diffusion (10.1109/TIFS.2024.3444311)\n- Generative Image Steganography Based on Text-to-Image Multimodal Generative Model (10.1109/TCSVT.2025.3556892)\n- DGADM-GIS: Deterministic Guided Additive Diffusion Model for Generative Image Steganography (10.1109/TDSC.2025.3578676)\n- Diffusion-Stego: Training-free diffusion generative steganography via message projection (10.1016/j.ins.2025.122358)\n- Provably Secure Covert Messaging Using Image-Based Diffusion Processes (10.1109/SaTML64287.2025.00057)\n- StegaFDS: Generative Steganography Based on First-Order DPM-Solver (10.1109/TrustCom63139.2024.00042)\n- Conditional Flow-based Generative Steganography (10.1109/TDSC.2025.3570468)\nSince the proposed method looks generic for any LDM, it seems important to comprehensively discuss, compare and demonstrate over these prior works. Moreover, the proposed parameter modulation strategy looks very similar to the above works, so it is important to distinguish the proposed method over them in the setting of LDM.\n2) Even though 12,000 bits of data into a 5-sec video may be great, it seems also possible to embed messages per image using any above image-based methods, which can embed 1 bpp with 100% accuracy - of course it may fail for video compression. However, there is no demonstration for it, so it is difficult to see the proposed method as a SOTA. Thus, the proposed method should be compared with other image-based methods as well as other video steganography method if possible, such as\n- Large-capacity and Flexible Video Steganography via Invertible Neural Network (CVPR 2023)\n- StegaNeRV: Video Steganography using Implicit Neural Representation (CVPRW 2024)\n- Efficient three layer secured adaptive video steganography method using chaotic dynamic systems (Scientific Reports 2024)\n\nQUESTIONS:\nPlease address the major concerns above.\n\nIt is unclear if multi-mask is robust for any video contents. Any discussion?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-04"}