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.

翻译：对生成图像进行高效可靠的检测对于负责任地部署生成模型至关重要。现有方法主要致力于提升检测精度以及在各种图像变换和对抗性操作下的鲁棒性，但很大程度上忽视了在大规模图像集合中进行水印检测的效率挑战。为弥补这一不足，我们提出了QRMark，一种高效自适应的端到端方法，用于检测嵌入的图像水印。QRMark的核心思想是将受二维码启发的纠错机制与定制的分块技术相结合，在保持精度和鲁棒性的同时提升检测效率。在算法层面，QRMark采用里德-所罗门纠错机制来缓解分块引入的精度下降。在系统层面，QRMark实现了一种资源感知的多通道水平融合策略，能自适应地为检测流程中GPU密集型阶段分配更多计算流。它还采用基于分块的工作负载交错策略，将数据加载开销与计算重叠，并在各阶段间调度内核以最大化效率。端到端评估表明，QRMark相比顺序基线实现了平均2.43倍的推理加速。