MOLM: Mixture of LoRA Markers

• URL: http://arxiv.org/abs/2510.00293v1
• Date: Tue, 30 Sep 2025 21:27:14 GMT
• Title: MOLM: Mixture of LoRA Markers
• Abstract summary: We propose a general watermarking framework that formulates the encoding problem as key-dependent of the parameters of a generative model.<n>Within this framework, we introduce Mixture of LoRA Markers (MOLM), a routing-based instantiation in which binary keys activate lightweight LoRA adapters inside residual and attention blocks.
• Score: 28.772539229108286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models can generate photorealistic images at scale. This raises urgent concerns about the ability to detect synthetically generated images and attribute these images to specific sources. While watermarking has emerged as a possible solution, existing methods remain fragile to realistic distortions, susceptible to adaptive removal, and expensive to update when the underlying watermarking key changes. We propose a general watermarking framework that formulates the encoding problem as key-dependent perturbation of the parameters of a generative model. Within this framework, we introduce Mixture of LoRA Markers (MOLM), a routing-based instantiation in which binary keys activate lightweight LoRA adapters inside residual and attention blocks. This design avoids key-specific re-training and achieves the desired properties such as imperceptibility, fidelity, verifiability, and robustness. Experiments on Stable Diffusion and FLUX show that MOLM preserves image quality while achieving robust key recovery against distortions, compression and regeneration, averaging attacks, and black-box adversarial attacks on the extractor.

Related papers

• On the Robustness of Watermarking for Autoregressive Image Generation [21.402408065930796]
  Autoregressive (AR) image generators demand reliable detection and attribution of their outputs to prevent model collapse.<n>Watermarking techniques, specifically designed for AR models, embed a subtle signal at generation time, enabling downstream verification.<n>We study these schemes and demonstrate their vulnerability to both watermark removal and forgery attacks.
  arXiv  Detail & Related papers  (2026-04-13T16:56:48Z)
• SemBind: Binding Diffusion Watermarks to Semantics Against Black-Box Forgery Attacks [74.76909939060833]
  Black-box forgery attacks pose outsized risk to provenance and trust.<n>We propose SemBind, a framework for latent-based watermarks that resists black-box forgery.<n>We show that SemBind-enabled anti-forgery variants markedly reduce false acceptance under black-box forgery.
  arXiv  Detail & Related papers  (2026-01-28T07:02:40Z)
• AuthenLoRA: Entangling Stylization with Imperceptible Watermarks for Copyright-Secure LoRA Adapters [52.556959321030966]
  Low-Rank Adaptation (LoRA) offers an efficient paradigm for customizing diffusion models.<n>Existing watermarking techniques either target base models or verify LoRA modules themselves.<n>We propose AuthenLoRA, a unified watermarking framework that embeds imperceptible, traceable watermarks directly into the LoRA training process.
  arXiv  Detail & Related papers  (2025-11-26T09:48:11Z)
• Active Adversarial Noise Suppression for Image Forgery Localization [56.98050814363447]
  We introduce an Adversarial Noise Suppression Module (ANSM) that generate a defensive perturbation to suppress the attack effect of adversarial noise.<n>To our best knowledge, this is the first report of adversarial defense in image forgery localization tasks.
  arXiv  Detail & Related papers  (2025-06-15T14:53:27Z)
• A Watermark for Auto-Regressive Image Generation Models [50.599325258178254]
  We propose C-reweight, a distortion-free watermarking method explicitly designed for image generation models.<n>C-reweight mitigates retokenization mismatch while preserving image fidelity.
  arXiv  Detail & Related papers  (2025-06-13T00:15:54Z)
• Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models [66.54457339638004]
  Copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models.<n>We propose a diffusion model watermarking method tailored for real-world deployment.<n>Gaussian Shading++ not only maintains performance losslessness but also outperforms existing methods in terms of robustness.
  arXiv  Detail & Related papers  (2025-04-21T11:18:16Z)
• Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal [57.84348166457113]
  We introduce a novel feature adapting framework that leverages the representation capacity of a pre-trained image inpainting model.<n>Our approach bridges the knowledge gap between image inpainting and watermark removal by fusing information of the residual background content beneath watermarks into the inpainting backbone model.<n>For relieving the dependence on high-quality watermark masks, we introduce a new training paradigm by utilizing coarse watermark masks to guide the inference process.
  arXiv  Detail & Related papers  (2025-04-07T02:37:14Z)
• Safe-VAR: Safe Visual Autoregressive Model for Text-to-Image Generative Watermarking [18.251123923955397]
  Autoregressive learning has become a dominant approach for text-to-image generation, offering high efficiency and visual quality.<n>Existing watermarking methods, designed for diffusion models, often struggle to adapt to the sequential nature of VAR models.<n>We propose Safe- VAR, the first watermarking framework specifically designed for autoregressive text-to-image generation.
  arXiv  Detail & Related papers  (2025-03-14T11:45:10Z)
• AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA [67.68750063537482]
  Diffusion models have achieved remarkable success in generating high-quality images. Recent works aim to let SD models output watermarked content for post-hoc forensics. We propose textttmethod as the first implementation under this scenario.
  arXiv  Detail & Related papers  (2024-05-18T01:25:47Z)
• MarkPlugger: Generalizable Watermark Framework for Latent Diffusion Models without Retraining [48.41130825143742]
  In the fast-evolving era of AI-generated content (AIGC), the rapid iteration and modification of latent diffusion models (LDMs) makes retraining with watermark models costly.<n>We propose MarkPlugger, a generalizable plug-and-play watermark framework without LDM retraining.<n>Our experimental findings reveal that our method effectively harmonizes image quality and watermark recovery rate.
  arXiv  Detail & Related papers  (2024-04-08T15:29:46Z)
• Wide Flat Minimum Watermarking for Robust Ownership Verification of GANs [23.639074918667625]
  We propose a novel multi-bit box-free watermarking method for GANs with improved robustness against white-box attacks. The watermark is embedded by adding an extra watermarking loss term during GAN training. We show that the presence of the watermark has a negligible impact on the quality of the generated images.
  arXiv  Detail & Related papers  (2023-10-25T18:38:10Z)

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.