Identifying recaptured images using deep hybrid correlation network

With the explosive advancements in image display technology, recapturing high-quality images from high-fidelity LCD screens becomes more and more easy. Such recaptured images can not only be used for deceiving intelligent recognition systems but also for hiding tampering traces. In order to prevent such a security loophole, we propose a recaptured image detection approach based on deep hybrid correlation network. Specifically, we first design a deep hybrid correlation module to extract the correlations in different color channels and neighboring pixels. This module has three different branches, in which a 1×1 convolution layer is used to learn the correlations between color channels while two consecutive convolution sub-modules are used to extract the correlations between neighboring pixels. Then we feed the output of this module into consecutive convolution modules to further learn the hierarchical representation for make decision. Ablation experiments verify the effectiveness of our proposed deep hybrid correlation module, while single database experiments demonstrate that our proposed method can achieve average accuracy with about 99% on three public databases. Specifically, our method not only performs very close to the state-of-the-art methods on the most difficult-to-detect ICL-COMMSP database and the relative low-quality NTU-ROSE database, but also improves the performance on the most diverse Dartmouth database obviously, which verifies the effectiveness of the proposed deep architecture. Besides, mixed database experiments verify the superiority of the generalization ability of our proposed method.