# README: Robust Error-Aware Digital Signature Framework via Deep Watermarking Model

arXiv: https://arxiv.org/abs/2507.04495 · HTML: https://mlsanity.com/paper/2507.04495/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 6 Jul 2025.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

## Builds on (most cited first, 9 of 9)

- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [StegaStamp: Invisible Hyperlinks in Physical Photographs](https://mlsanity.com/paper/1904.05343/) (2019)
- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression](https://mlsanity.com/paper/2108.08211/) (2021)
- [End-to-end Trained CNN Encode-Decoder Networks for Image Steganography](https://mlsanity.com/paper/1711.07201/) (2017)
- [On the Difficulty of Constructing a Robust and Publicly-Detectable Watermark](https://mlsanity.com/paper/2502.04901/) (2025)
- [Hybrid blind robust image watermarking technique based on DFT-DCT and Arnold transform](https://mlsanity.com/paper/1911.00753/) (2019)
- [Deep Learning-based Dual Watermarking for Image Copyright Protection and Authentication](https://mlsanity.com/paper/2502.18501/) (2025)
- [SCReedSolo: A Secure and Robust LSB Image Steganography Framework with Randomized Symmetric Encryption and Reed-Solomon Coding](https://mlsanity.com/paper/2503.12368/) (2025)

## Abstract (arXiv)

Deep learning-based watermarking has emerged as a promising solution for robust image authentication and protection. However, existing models are limited by low embedding capacity and vulnerability to bit-level errors, making them unsuitable for cryptographic applications such as digital signatures, which require over 2048 bits of error-free data. In this paper, we propose README (Robust Error-Aware Digital Signature via Deep WaterMarking ModEl), a novel framework that enables robust, verifiable, and error-tolerant digital signatures within images. Our method combines a simple yet effective cropping-based capacity scaling mechanism with ERPA (ERror PAinting Module), a lightweight error correction module designed to localize and correct bit errors using Distinct Circular Subsum Sequences (DCSS). Without requiring any fine-tuning of existing pretrained watermarking models, README significantly boosts the zero-bit-error image rate (Z.B.I.R) from 1.2% to 86.3% when embedding 2048-bit digital signatures into a single image, even under real-world distortions. Moreover, our use of perceptual hash-based signature verification ensures public verifiability and robustness against tampering. The proposed framework unlocks a new class of high-assurance applications for deep watermarking, bridging the gap between signal-level watermarking and cryptographic security.