Recurrent Comparator with Attention Models to Detect Counterfeit Documents This paper is focused on the detection of counterfeit documents via the recurrent comparison of the security textured background regions of two images. The main contributions are twofold: first we apply and adapt a recurrent comparator architecture with attention mechanism to the counterfeit detection task, which constructs a representation of the background regions by recurrently condition the next observation, learning the difference between genuine and counterfeit images through iterative glimpses. Second we propose a new counterfeit document dataset to ensure the generalization of the learned model towards the detection of the lack of resolution during the counterfeit manufacturing. The presented network, outperforms stateoftheart classification approaches for counterfeit detection as demonstrated in the evaluation.

Characters Verification via Siamese Convolutional Neural Network In the printing and carving industries, it is necessary to check whether printed outputs or carved wares are missing or etched through comparing the drawings. Traditional approaches and identification methods canxe2x80x99t be used for this application where the number of character categories are not determined, and where the character may be unique designed by manufacturer. Driven by the onetoone matching pattern, we propose an endtoend dual input network for automatic comparison, which uses convolutional neural network to extract features from the scanned images which collected from printed matters. Then, we convert the corresponding drawing to the vector of the same dimension to calculate distance and the matchmismatch result. Experiments show that our method can effectively solve the problem of character comparison with many types, and at the same time propose an automated comparing program for the industrial imprinting process.