Near-duplicate images are often generated when applying repeated photometric and geometric transformations that produce imperceptible variants of the original image. Consequently, a deluge of near-duplicates can be circulated online posing copyright infringement concerns. The concerns are more severe when biometric data is altered through such nuanced transformations. In this work, we address the challenge of near-duplicate detection in face images by, firstly, identifying the original image from a set of near-duplicates and, secondly, deducing the relationship between the original image and the near-duplicates. We construct a tree-like structure, called an Image Phylogeny Tree (IPT) using a graph-theoretic approach to estimate the relationship, i.e., determine the sequence in which they have been generated. We further extend our method to create an ensemble of IPTs known as Image Phylogeny Forests (IPFs). We rigorously evaluate our method to demonstrate robustness across other modalities, unseen transformations by latest generative models and IPT configurations, thereby significantly advancing the state-of-the-art performance by 42% on IPF reconstruction accuracy.

翻译：近重复图像通常是通过对原始图像施加重复的光度与几何变换而产生的，这些变换会生成与原始图像难以察觉的变体。因此，大量近重复图像可能在网络传播，引发版权侵权担忧。当生物特征数据通过此类细微变换被篡改时，问题尤为严重。在本研究中，我们通过两个步骤应对人脸图像近重复检测的挑战：首先，从一组近重复图像中识别原始图像；其次，推断原始图像与近重复图像之间的关联关系。我们采用图论方法构建了一种称为图像谱系树（IPT）的树状结构，以估计其关联关系，即确定它们被生成的顺序。我们进一步扩展该方法，构建了由多个IPT组成的集成结构，称为图像谱系森林（IPFs）。我们对该方法进行了严格评估，证明了其在其他模态、最新生成模型未见过的变换以及不同IPT配置下的鲁棒性，从而将IPF重建准确率的最先进性能显著提升了42%。