README: Robust Error-Aware Digital Signature Framework via Deep Watermarking Model
Hyunwook Choi, Sangyun Won, Daeyeon Hwang, Junhyeok Choi

TL;DR
README introduces a novel deep watermarking framework that enables error-tolerant, verifiable digital signatures in images, significantly improving capacity and robustness for cryptographic applications without fine-tuning existing models.
Contribution
The paper presents README, a framework combining capacity scaling and error correction modules to enable high-capacity, error-tolerant digital signatures in images using deep watermarking.
Findings
Zero-bit-error image rate increased from 1.2% to 86.3%.
Supports embedding 2048-bit signatures with high robustness.
No fine-tuning required for existing watermarking models.
Abstract
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…
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