In this paper we introduce a new digital image forensics approach called
forensic similarity, which determines whether two image patches contain the
same forensic trace or different forensic traces. One benefit of this approach
is that prior knowledge, e.g. training samples, of a forensic trace are not
required to make a forensic similarity decision on it in the future. To do
this, we propose a two part deep-learning system composed of a CNN-based
feature extractor and a three-layer neural network, called the similarity
network. This system maps pairs of image patches to a score indicating whether
they contain the same or different forensic traces. We evaluated system
accuracy of determining whether two image patches were 1) captured by the same
or different camera model, 2) manipulated by the same or different editing
operation, and 3) manipulated by the same or different manipulation parameter,
given a particular editing operation. Experiments demonstrate applicability to
a variety of forensic traces, and importantly show efficacy on "unknown"
forensic traces that were not used to train the system. Experiments also show
that the proposed system significantly improves upon prior art, reducing error
rates by more than half. Furthermore, we demonstrated the utility of the
forensic similarity approach in two practical applications: forgery detection
and localization, and database consistency verification.

介绍一种新的数字图像取证方法，称为 “取证相似性”，可以确定两个图像图块是否包含相同的取证痕迹或不同的取证痕迹。该方法使用基于卷积神经网络的特征提取器和三层神经网络组成的系统来计算图像汇总所包含的同一或不同取证痕迹的相似性得分，实验证明其可应用于各种取证痕迹，不需要事先了解特定痕迹的知识。该系统在检测相机型号、图像编辑和操作参数方面，表现出优异的性能和应用前景。