Image Copy Detection (ICD) aims to identify manipulated content between image pairs through robust feature representation learning. While self-supervised learning (SSL) has advanced ICD systems, existing view-level contrastive methods struggle with sophisticated edits due to insufficient fine-grained correspondence learning. We address this limitation by exploiting the inherent geometric traceability in edited content through two key innovations. First, we propose PixTrace - a pixel coordinate tracking module that maintains explicit spatial mappings across editing transformations. Second, we introduce CopyNCE, a geometrically-guided contrastive loss that regularizes patch affinity using overlap ratios derived from PixTrace's verified mappings. Our method bridges pixel-level traceability with patch-level similarity learning, suppressing supervision noise in SSL training. Extensive experiments demonstrate not only state-of-the-art performance (88.7% uAP / 83.9% RP90 for matcher, 72.6% uAP / 68.4% RP90 for descriptor on DISC21 dataset) but also better interpretability over existing methods.

翻译：图像复制检测旨在通过鲁棒的特征表示学习识别图像对之间的篡改内容。尽管自监督学习推动了复制检测系统的发展，但现有的视图级对比方法由于细粒度对应学习不足，难以应对复杂的编辑操作。我们通过利用编辑内容中固有的几何可追踪性，提出两项关键创新来解决这一局限。首先，我们提出PixTrace——一个像素坐标追踪模块，可在编辑变换过程中保持显式的空间映射关系。其次，我们引入CopyNCE，这是一种几何引导的对比损失函数，利用PixTrace验证映射得出的重叠率来规范化块间亲和性。我们的方法将像素级可追踪性与块级相似性学习相结合，有效抑制了自监督学习训练中的监督噪声。大量实验表明，该方法不仅在DISC21数据集上取得了最先进的性能（匹配器：88.7% uAP / 83.9% RP90；描述器：72.6% uAP / 68.4% RP90），而且相比现有方法具有更好的可解释性。