Manipulation and re-use of images in scientific publications is a concerning problem that currently lacks a scalable solution. Current tools for detecting image duplication are mostly manual or semi-automated, despite the availability of an overwhelming target dataset for a learning-based approach. This paper addresses the problem of determining if, given two images, one is a manipulated version of the other by means of copy, rotation, translation, scale, perspective transform, histogram adjustment, or partial erasing. We propose a data-driven solution based on a 3-branch Siamese Convolutional Neural Network. The ConvNet model is trained to map images into a 128-dimensional space, where the Euclidean distance between duplicate images is smaller than or equal to 1, and the distance between unique images is greater than 1. Our results suggest that such an approach has the potential to improve surveillance of the published and in-peer-review literature for image manipulation.
In @cite_4 , the authors improved upon the standard siamese network model by adding one extra branch, thus training on image triplets instead of pairs. A triplet consists of an anchor, a positive example ( same'' or similar'' to the anchor image), and a negative example ( different'' from the anchor). A triplet loss was designed to drive similar images to be nearby, and dissimilar images to be far apart, encouraging the embedding space to be locally Euclidean. A clever trick that enables fast convergence is the use of hard negative mining: selecting examples where different'' images are close according to the current metric, and similar'' images are far apart.
@cite_4
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Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. In this paper we present a system, called FaceNet, that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure of face similarity. Once this space has been produced, tasks such as face recognition, verification and clustering can be easily implemented using standard techniques with FaceNet embeddings as feature vectors.