Datasetpublic

shifangming0823/AuthenLoRA_Dataset

Official Model Repository for Paper AuthenLoRA: Entangling Stylization with Imperceptible Watermarks for Copyright-Secure LoRA Adapters You can find the Pytorch implementation in Github and model weights in Huggingface Method In this work, we propose AuthenLoRA, a novel watermarking method that effectively protects the copyright of LoRA models by embedding imperceptible watermarks during the generation of stylized images. We jointly optimize style transfer and watermark… See the full description on the dataset page: https://huggingface.co/datasets/shifangming0823/AuthenLoRA_Dataset.

sourceHugging Faceupdated 10mo agoView on Hugging Face

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Dataset Card

Official Model Repository for Paper [AuthenLoRA: Entangling Stylization with Imperceptible Watermarks for Copyright-Secure LoRA Adapters](https://arxiv.org/abs/2511.21216)

You can find the Pytorch implementation in [Github](https://github.com/ShiFangming0823/AuthenLoRA) and model weights in [Huggingface](https://huggingface.co/shifangming0823/AuthenLoRA_model)

Method

In this work, we propose AuthenLoRA, a novel watermarking method that effectively protects the copyright of LoRA models by embedding imperceptible watermarks during the generation of stylized images.

We jointly optimize style transfer and watermark embedding to achieve tight coupling between the two objectives. A lightweight mapper is introduced to modulate LoRA parameters, enabling dynamic modification of embedded watermarks without retraining. To suppress perceptual watermark residual and reduce false positives, we design the Perceptual Residual Tone Suppression (PRTS) loss and the Zero-Message Regularization strategy. Furthermore, we incorporate ResNet blocks into the fine-tuning scope, significantly improving training efficiency and watermark effectiveness.

We hope our work contributes to the advancement of digital asset copyright protection. :)

Visual Demonstration