Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal (2504.04687v1)

Abstract: Visible watermark removal which involves watermark cleaning and background content restoration is pivotal to evaluate the resilience of watermarks. Existing deep neural network (DNN)-based models still struggle with large-area watermarks and are overly dependent on the quality of watermark mask prediction. To overcome these challenges, we introduce a novel feature adapting framework that leverages the representation modeling capacity of a pre-trained image inpainting model. 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. We establish a dual-branch system to capture and embed features from the residual background content, which are merged into intermediate features of the inpainting backbone model via gated feature fusion modules. Moreover, 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. This contributes to a visible image removal model which is insensitive to the quality of watermark mask during testing. Extensive experiments on both a large-scale synthesized dataset and a real-world dataset demonstrate that our approach significantly outperforms existing state-of-the-art methods. The source code is available in the supplementary materials.

Bridging Knowledge Gap Between Image Inpainting and Large-Area Visible Watermark Removal

The paper presents a novel approach to address the challenges of visible watermark removal, particularly focusing on large-area watermarks that pose significant complexities in background content restoration. Traditional methods relying on deep neural networks (DNNs) face two main issues: dependency on high-quality watermark masks and difficulty handling large-area watermarks. This paper introduces a feature adaptation framework that integrates image inpainting techniques with watermark removal processes, highlighting its potential to overcome these obstacles.

Methodology Overview

The authors propose leveraging a pre-trained image inpainting model, LaMa, renowned for its resolution robustness and fast Fourier convolution methods. The novelty lies in effectively merging the residual background content beneath watermarks with the model's intermediate features. The framework employs a dual-branch system comprising two main structures:

1. Watermark Component Cleaning Branch (WCC): This branch focuses on removing watermark interference from input images, employing transposed attention modules to capture and enhance global contextual information. By subtracting watermark components, the WCC branch ensures the preservation of residual background content, providing essential features for subsequent restoration processes.
2. Background Content Embedding Branch (BCE): The second branch enriches the input of the cleaned background image with additional original input to embed pertinent background features. Similar to WCC, it utilizes transposed attention modules for feature extraction, ensuring that comprehensive background information supports the accurate reconstruction of destroyed regions.

The authors further innovate by using gated fusion modules (GFM) to adapt the LaMa model effectively. The GFM integrates features extracted from both branches into LaMa's intermediate FFC module outputs, refining the model's capability for high-quality background restoration.

Handling Coarse Watermark Masks

Recognizing the challenge of achieving precise watermark segmentation, the paper shifts focus from high-quality masks to coarse ones. During training, the model adapts by augmentedly coarse masks that offer moderate identification of watermark regions. This paradigm fosters a model resilient to varying watermark mask quality, exhibiting robustness in real-world applications where watermark segmentation results are often imperfect.

Experimental Evaluation and Results

Extensive experiments were conducted using the newly introduced Images with Large-Area Watermarks (ILAW) dataset and a collection of real-world images. The proposed method demonstrated superior performance metrics, including PSNR and SSIM. Experimental comparisons against state-of-the-art watermark removal techniques such as SplitNet, SLBR, and image inpainting models like LaMa underscore its efficacy. Additionally, qualitative evaluations reveal the model's ability to effectively eliminate visible watermark traces while accurately recovering lost background content.

Implications and Future Directions

The fusion of image inpainting models with watermark removal processes presents significant implications for fields where image authenticity is paramount, such as forensic analysis and media restoration. By reducing reliance on high-quality watermark masks, this approach broadens practical applicability across diverse environments, including those with limited computational resources.

Future research could expand the exploration of adaptive feature fusion techniques and the incorporation of complementary data modalities to further enhance watermark removal efficacy. Moreover, analyzing model scalability across varied image dimensions and complexities could provide deeper insights into optimizing this framework for broader industry applications. The intersection of AI-driven image restoration and watermark resilience evaluation remains a promising avenue for advancing both theoretical understanding and practical deployment in digital content processing domains.