An Efficient and Adaptive Watermark Detection System with Tile-based Error Correction
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917140422983680 |
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| author | Zhong, Xinrui Feng, Xinze Zuo, Jingwei Ye, Fanjiang Mu, Yi Guo, Junfeng Huang, Heng Lee, Myungjin Wang, Yuke |
| author_facet | Zhong, Xinrui Feng, Xinze Zuo, Jingwei Ye, Fanjiang Mu, Yi Guo, Junfeng Huang, Heng Lee, Myungjin Wang, Yuke |
| contents | Efficient and reliable detection of generated images is critical for the responsible deployment of generative models. Existing approaches primarily focus on improving detection accuracy and robustness under various image transformations and adversarial manipulations, yet they largely overlook the efficiency challenges of watermark detection across large-scale image collections. To address this gap, we propose QRMark, an efficient and adaptive end-to-end method for detecting embedded image watermarks. The core idea of QRMark is to combine QR Code-inspired error correction with tailored tiling techniques to improve detection efficiency while preserving accuracy and robustness. At the algorithmic level, QRMark employs a Reed-Solomon error correction mechanism to mitigate the accuracy degradation introduced by tiling. At the system level, QRMark implements a resource-aware multi-channel horizontal fusion policy that adaptively assigns more streams to GPU-intensive stages of the detection pipeline. It further employs a tile-based workload interleaving strategy to overlap data-loading overhead with computation and schedules kernels across stages to maximize efficiency. End-to-end evaluations show that QRMark achieves an average 2.43x inference speedup over the sequential baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02447 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Efficient and Adaptive Watermark Detection System with Tile-based Error Correction Zhong, Xinrui Feng, Xinze Zuo, Jingwei Ye, Fanjiang Mu, Yi Guo, Junfeng Huang, Heng Lee, Myungjin Wang, Yuke Distributed, Parallel, and Cluster Computing Efficient and reliable detection of generated images is critical for the responsible deployment of generative models. Existing approaches primarily focus on improving detection accuracy and robustness under various image transformations and adversarial manipulations, yet they largely overlook the efficiency challenges of watermark detection across large-scale image collections. To address this gap, we propose QRMark, an efficient and adaptive end-to-end method for detecting embedded image watermarks. The core idea of QRMark is to combine QR Code-inspired error correction with tailored tiling techniques to improve detection efficiency while preserving accuracy and robustness. At the algorithmic level, QRMark employs a Reed-Solomon error correction mechanism to mitigate the accuracy degradation introduced by tiling. At the system level, QRMark implements a resource-aware multi-channel horizontal fusion policy that adaptively assigns more streams to GPU-intensive stages of the detection pipeline. It further employs a tile-based workload interleaving strategy to overlap data-loading overhead with computation and schedules kernels across stages to maximize efficiency. End-to-end evaluations show that QRMark achieves an average 2.43x inference speedup over the sequential baseline. |
| title | An Efficient and Adaptive Watermark Detection System with Tile-based Error Correction |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.02447 |