# SpreadMark: Robust Image Watermarking via Spread-Spectrum Embedding

arXiv: https://arxiv.org/abs/2608.03165 · HTML: https://mlsanity.com/paper/2608.03165/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 39 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Builds on (most cited first, 24 of 24)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [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)
- [Robust Invisible Video Watermarking with Attention](https://mlsanity.com/paper/1909.01285/) (2019)
- [Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models](https://mlsanity.com/paper/2404.04956/) (2024)
- [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)
- [Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances](https://mlsanity.com/paper/2410.18775/) (2024)
- [WAVES: Benchmarking the Robustness of Image Watermarks](https://mlsanity.com/paper/2401.08573/) (2024)
- [Watermarking Images in Self-Supervised Latent Spaces](https://mlsanity.com/paper/2112.09581/) (2021)
- [RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identification](https://mlsanity.com/paper/2404.14055/) (2024)
- [Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms](https://mlsanity.com/paper/2212.12678/) (2022)
- [SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection](https://mlsanity.com/paper/2305.06321/) (2023)
- [Attack-Resilient Image Watermarking Using Stable Diffusion](https://mlsanity.com/paper/2401.04247/) (2024)
- [Practical Deep Dispersed Watermarking with Synchronization and Fusion](https://mlsanity.com/paper/2310.14532/) (2023)
- [RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees](https://mlsanity.com/paper/2403.18774/) (2024)
- [Breaking Semantic-Aware Watermarks via LLM-Guided Coherence-Preserving Semantic Injection](https://mlsanity.com/paper/2602.21593/) (2026)
- [Robust Spatial-spread Deep Neural Image Watermarking](https://mlsanity.com/paper/2005.11735/) (2020)
- [SemBind: Binding Diffusion Watermarks to Semantics Against Black-Box Forgery Attacks](https://mlsanity.com/paper/2601.20310/) (2026)
- [Fast, Secure, and High-Capacity Image Watermarking with Autoencoded Text Vectors](https://mlsanity.com/paper/2510.00799/) (2025)
- [DeMark: A Query-Free Black-Box Attack on Deepfake Watermarking Defenses](https://mlsanity.com/paper/2601.16473/) (2026)
- [Decoupling Defense Strategies for Robust Image Watermarking](https://mlsanity.com/paper/2602.20053/) (2026)

## Abstract (arXiv)

Invisible image watermarks are increasingly used for deepfake detection and provenance tracking, where they must survive not only incidental distortions but also deliberate removal. We revisit spread-spectrum embedding, a classical watermarking principle, inside a modern neural post-hoc watermarking architecture. Our starting point is a measurement: in existing encoder-decoder schemes each message bit occupies only a small fraction of the image, a shared contributing factor to their fragility, since removal then need only disturb the region a bit occupies. SpreadMark instead spreads each bit as a dense pseudo-random codeword over the whole image and recovers it by matched-filtering a learned cover-suppressed chip representation, with a parallel convolutional decoding path and sparsification-aware training. A conditional chip-space analysis shows that, under a codeword-independent perturbation model, dense spreading increases the budget required to disrupt matched-filter recovery. Evaluated on COCO and DIV2K against nine schemes, SpreadMark is the only evaluated method retaining high detection under both the regeneration and the latent-space sparsification settings we test, with competitive JPEG and additive-noise robustness. It keeps the embedded watermark imperceptible, maintaining high perceptual quality on both COCO and DIV2K.