This report describes an ongoing project initiated in late 2012 to investigate the use of chain line pattern (CLiP) matching for the detection of mould mates (sheets made from the same papermaking mould) in the European paper used for printing Rembrandt’s etchings. We investigate the application of computer-based image processing tools to mark, measure, and compare the idiosyncratic intervals of chain lines as recorded in beta-radiographs of individual sheets in the hunt for mouldmates. Results indicate that CLiP has strong potential for investigating and matching papers used by Rembrandt and other early modern printmakers. It provides a new method that can identify related objects even in the absence of a watermark.

This paper investigates the reconstruction of Van Gogh’s drawings which have been degraded in the course of time due to aging problems, like ink fading and discoloration. Learning to predict the past and original appearances of degraded drawings can help to envisage how the artist’s work may have looked at the time of creation. In this paper, we use reproductions as reference information for the past appearances of drawings and consider the reconstruction of drawings as a pixel-wise prediction problem. We present an approach to automatically predict the past appearances of drawings. This approach brings together methods from multi-resolution image analysis and deep convolutional neural networks (CNNs) for addressing the task of pixel-wise prediction. Our experiments first investigate how scale affects prediction performance of the proposed multi-scale CNN framework and then demonstrate the reconstruction capability of the multi-scale CNN framework. The results demonstrate that the predictive reconstruction of degraded images is a feasible endeavor.

Widespread adoption of RFID technology is being slowed down because of increasing public concerns about associated security threats. This paper shows that it is possible to enhance the security of RFID systems by requiring readers to perform a computational effort test. Readers must solve a cryptographic puzzle - one of the components of the Weakly Secret Bit Commitment (WSBC) sent by tags - to obtain the static identifier of the interrogated tag. The method we present is based on a simple concept already used in security applications such as anti-spam or TCP SYN flooding protection, yet original in the RFID context until now. The scheme provides privacy protection while being an effective countermeasure against the indiscriminate disclosure of the whole contents of a large number of tags. Then, we scrutinize the combined use of cryptographic puzzles and distance-bounding protocols. First, a classical and relatively straight-forward solution is presented. Secondly, we introduce a protocol named Noent, that follows a new approach that reduces drawbacks associated with WSBC such as key delegation, whilst gaining all the advantages of employing distancebounding protocols such as the certainty on the distance between a tag and reader.

In RFID protocols, random numbers are mainly required to anonymize tag answers in order to guarantee the privacy of the owner of the transponder. Our analysis looks at the feasibility of RFID tags for supporting Cryptographically Secure Pseudorandom Number Generators (CS-PRNG) on their limited chip. Specifically, we study the implementation of the Blum-Blum-Shub (BBS) pseudorandom number generator for security levels 2³² (160 bits) and 2⁶⁴ (512 bits) respectively, these values being suitable for many RFID applications but not for standard security applications.

The digital analysis of the paper structure can play a major role in the authentication, dating and attribution of art which is very important for art historians. In this paper, a digital analysis method is introduced for the extraction of structural features from machine-made papers. To generate digital images from these papers, we use a transmitted light scanning method; since this method is feasible and inexpensive as compared to other methods such as x-ray imaging. In machine-made papers, two types of structure on the surface of a paper can be found: regular (periodic) and irregular (non-periodic). In this paper, we show that the power spectrum in Fourier domain is an adequate way to extract structural features and isolate regular and irregular structures. The structural features can be further used for authentication and dating of machine-made papers. Our method is a simple and inexpensive method with respect to costs and computational efficiency and it is invariant to the direction in which the paper is scanned.