We present SImProv - a scalable image provenance framework to match a query image back to a trusted database of originals and identify possible manipulations on the query. SImProv consists of three stages: a scalable search stage for retrieving top-k most similar images; a re-ranking and near-duplicated detection stage for identifying the original among the candidates; and finally a manipulation detection and visualization stage for localizing regions within the query that may have been manipulated to differ from the original. SImProv is robust to benign image transformations that commonly occur during online redistribution, such as artifacts due to noise and recompression degradation, as well as out-of-place transformations due to image padding, warping, and changes in size and shape. Robustness towards out-of-place transformations is achieved via the end-to-end training of a differentiable warping module within the comparator architecture. We demonstrate effective retrieval and manipulation detection over a dataset of 100 million images.

翻译：我们提出SImProv——一种可扩展的图像溯源框架，能将查询图像与可信原始图像数据库进行匹配，并识别查询图像中可能存在的篡改操作。该框架包含三个阶段：用于检索前k个最相似图像的可扩展搜索阶段；用于从候选图像中识别原始图像的重新排序与近重复检测阶段；以及最后用于定位查询图像中可能经篡改而偏离原始图像区域的篡改检测与可视化阶段。SImProv对在线分发过程中常见良性图像变换具有鲁棒性，例如噪声与重压缩退化导致的伪影，以及因图像填充、扭曲和尺寸形状变化引起的非原位变换。通过对比较器架构内的可微分扭曲模块进行端到端训练，实现了对非原位变换的鲁棒性。我们在包含1亿张图像的数据集上验证了有效的检索与篡改检测性能。