SpreadMark: Robust Image Watermarking via Spread-Spectrum Embedding

• URL: http://arxiv.org/abs/2608.03165v1
• Date: Tue, 04 Aug 2026 05:52:17 GMT
• Title: SpreadMark: Robust Image Watermarking via Spread-Spectrum Embedding
• Abstract summary: We revisit spread-spectrum embedding, a classical watermarking principle, inside a modern neural post-hoc watermarking architecture.<n>Our starting point is a measurement: in existing encoder-decoder schemes each message bit occupies only a small fraction of the image.<n> SpreadMark 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.
• Score: 35.24760637584417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• T2SMark: Balancing Robustness and Diversity in Noise-as-Watermark for Diffusion Models [89.29541056113442]
  T2SMark is a two-stage watermarking scheme based on Tail-Truncated Sampling (TTS)<n>We evaluate T2SMark on diffusion models with both U-Net and DiT backbones.
  arXiv  Detail & Related papers  (2025-10-25T16:55:55Z)
• OptMark: Robust Multi-bit Diffusion Watermarking via Inference Time Optimization [66.69924980864053]
  We propose OptMark, an optimization-based approach that embeds a robust multi-bit watermark into the intermediate latents of the diffusion denoising process.<n> OptMark strategically inserts a structural watermark early to resist generative attacks and a detail watermark late to withstand image transformations.<n> Experimental results demonstrate that OptMark achieves invisible multi-bit watermarking while ensuring robust resilience against valuemetric transformations, geometric transformations, editing, and regeneration attacks.
  arXiv  Detail & Related papers  (2025-08-29T15:50:59Z)
• Towards Robust Red-Green Watermarking for Autoregressive Image Generators [17.784976310663104]
  In this paper, we explore the use of in-generation watermarks in autoregressive (AR) image models.<n>AR models generate images by autoregressively predicting a sequence of visual tokens that are then decoded into pixels.<n>Inspired by red-green watermarks for large language models, we examine token-level watermarking schemes that bias the next-token prediction.<n>We propose two novel watermarking methods that rely on visual token clustering to assign similar tokens to the same set.
  arXiv  Detail & Related papers  (2025-08-08T19:14:22Z)
• TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivity [76.98973481600002]
  This paper proposes a Tamper-Aware Generative image WaterMarking method named TAG-WM.<n>The proposed method comprises four key modules: a dual-mark joint sampling (DMJS) algorithm for embedding copyright and localization watermarks into the latent space while preserving generative quality.<n>The experimental results demonstrate that TAG-WM achieves state-of-the-art performance in both tampering robustness and localization capability even under distortion.
  arXiv  Detail & Related papers  (2025-06-30T03:14:07Z)
• Invisible Watermarks: Attacks and Robustness [0.3495246564946556]
  We introduce novel improvements to watermarking robustness and minimize degradation on image quality during attack.<n>We propose a custom watermark remover network which preserves one of the watermarking modalities while completely removing the other during decoding.<n>Our evaluation suggests that 1) implementing the watermark remover model to preserve one of the watermark modalities when decoding the other modality slightly improves on the baseline performance, and that 2) LBA degrades the image significantly less compared to uniform blurring of the entire image.
  arXiv  Detail & Related papers  (2024-12-17T03:50:13Z)
• RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees [33.61946642460661]
  This paper introduces a robust and agile watermark detection framework, dubbed as RAW. We employ a classifier that is jointly trained with the watermark to detect the presence of the watermark. We show that the framework provides provable guarantees regarding the false positive rate for misclassifying a watermarked image.
  arXiv  Detail & Related papers  (2024-01-23T22:00:49Z)
• Practical Deep Dispersed Watermarking with Synchronization and Fusion [10.633580224539337]
  We propose a practical deep textbfDispersed textbfWatermarking with textbfSynchronization and textbfFusion. Our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5.28% and 5.93% against single and combined attacks, respectively.
  arXiv  Detail & Related papers  (2023-10-23T03:34:05Z)
• Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust [55.91987293510401]
  Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. We introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed.
  arXiv  Detail & Related papers  (2023-05-31T17:00:31Z)
• Watermarking Images in Self-Supervised Latent Spaces [75.99287942537138]
  We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time.
  arXiv  Detail & Related papers  (2021-12-17T15:52:46Z)
• Robust Watermarking using Diffusion of Logo into Autoencoder Feature Maps [10.072876983072113]
  In this paper, we propose to use an end-to-end network for watermarking. We use a convolutional neural network (CNN) to control the embedding strength based on the image content. Different image processing attacks are simulated as a network layer to improve the robustness of the model.
  arXiv  Detail & Related papers  (2021-05-24T05:18:33Z)
• Split then Refine: Stacked Attention-guided ResUNets for Blind Single Image Visible Watermark Removal [69.92767260794628]
  Previous watermark removal methods require to gain the watermark location from users or train a multi-task network to recover the background indiscriminately. We propose a novel two-stage framework with a stacked attention-guided ResUNets to simulate the process of detection, removal and refinement. We extensively evaluate our algorithm over four different datasets under various settings and the experiments show that our approach outperforms other state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-12-13T09:05:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.