
Confidential Error-Correcting Codes
Introduces Confidential Error-correcting Codes (CECs), a primitive that decodes reliably from perturbed codewords while keeping the message confidential, defined by confidentiality, robustness and soundness. The Randomness-Security Separation framework encodes a nonce and masks the payload with a PRF; the Conc-CEC instantiation uses concatenated Reed-Muller and Reed-Solomon codes and claims stronger concrete security than PRCs (CRYPTO'24). The application, CECMark, watermarks AI-generated images and reportedly beats Gaussian Shading, PRCMark and T2SMark across 56 perturbations with a false positive rate below 1e-5 and no side effects.
What it claims
Confidential Error-correcting Codes (CECs) are a cryptographic primitive that decodes from perturbed codewords while keeping the message hidden. The abstract states that the concrete instantiation, Conc-CEC, has provable guarantees for all three properties: confidentiality, robustness and soundness. It also claims "significantly stronger concrete security guarantees" than PRCs (CRYPTO'24) "under practical parameter settings." The application is CECMark, an image watermark for AI-generated content. It is reported to beat Gaussian Shading (CVPR'24), PRCMark (ICLR'25) and T2SMark (NeurIPS'25) on robustness across all 56 evaluated perturbations, with a false positive rate below 10⁻⁵ and "without introducing side effects."
How it works
The construction follows a Randomness-Security Separation (RSS) structure, which splits randomness from secrecy:
- Encode a nonce, which supplies the randomness.
- Mask the encoded payload with a pseudorandom function (PRF), which supplies the secrecy.
Conc-CEC builds this from concatenated Reed-Muller and Reed-Solomon codes. The abstract does not say:
- which code is inner and which is outer;
- the code parameters;
- how the PRF key is managed;
- how CECMark embeds the codeword into images, for example in the latent or the noise of a diffusion model.
Limits
- Only the abstract is on the page. Everything above comes from it. Several things the paper itself may contain are not available here:
- the security bounds;
- the parameter choices behind the "stronger concrete security" claim against PRCs;
- the list of 56 perturbations;
- per-perturbation robustness numbers;
- which generative models were tested, described only as "real-world generative image models";
- what "side effects" means and how its absence was measured.
- Comparison not quantified. There is no number for the margin over the baselines, either on robustness or on concrete security.
- No code mentioned. The abstract does not mention a code or artifact release. Adopting CECMark currently means reimplementing it from the paper.
- Status. This is a preprint, received 2026-10-10 and approved on ePrint 2026-10-14. ePrint approval is not peer review.
- Licence. The paper is CC BY. That licence covers the text and says nothing about any implementation.
- Authors and scope. The authors are from Tsinghua and Shandong University, including Xiaoyun Wang. ePrint files the paper under Applications, with keywords watermarking, AIGC and code-based cryptography. The work is aimed at image watermarking; nothing in the abstract addresses text or other modalities.
Organisations named here
- Tsinghua University
- Shandong University
- CECMark
- Confidential Error-Correcting Codes
- Pseudorandom Codes (PRC)
Technologies named here
- CECMark
- Gaussian Shading
- PRCMark
- T2SMark
- pseudorandom function
- Reed-Muller codes
- Reed-Solomon codes
Techniques named here
- Randomness-Security Separation
- Conc-CEC
- CECMark
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