Estimating embedded data from clustered halftone dots via learned dictionary

Modulating the orientation of elliptically clustered dots in each halftone cell enables binary data to be embedded into the clustered halftone dots. In this paper, a new decoding method is proposed for recovering hidden binary data from clustered halftone dots by using learned dictionaries, which are optimized to represent clustered dots with different elliptical shapes. The basic idea is that the reconstruction errors of the clustered dots in a halftone cell are differentiable according to the dictionaries used. The experimental results showed that determining which of the learned dictionaries provides a minimum reconstruction error in a halftone cell can reveal the orientation of the clustered dots and thus indicate the embedded binary data. The experiment results also showed that the proposed decoding method can be applied to other types of clustered halftone dots to infer hidden binary data.