Distinguishing between natural and recolored images via lateral chromatic aberration

Abstract

With the development of image colorization technique, the recolored images (RIs) become more and more authentic, making it very difficult to visually distinguish from natural images (NIs). Recently, researchers have proposed the detection methods towards recolored images. However, the current detection still has limitations such as poor generalization, large-scale training samples, high-dimensional features for training, and high computation cost. To address those issues, this paper proposes a novel method based on the lateral chromatic aberration (LCA) inconsistency and its statistical differences. Generally, RIs have fewer numbers of LCA characteristics than that of NIs, that inspire us to design the classifier for distinguishing two types of images. In particular, we propose to adopt very low 5-dimensional features to feed a classical SVM mechanism. The baseline ImageNet and Oxford datasets are used to verify the effectiveness of the proposed method, in which the performance of our proposed method rivals the prior arts.