International audienceThe quick response (QR) code was designed for storage information and high-speed reading applications. In this paper, we present a new rich QR code that has two storage levels and can be used for document authentication. This new rich QR code, named two-level QR code, has public and private storage levels. The public level is the same as the standard QR code storage level; therefore, it is readable by any classical QR code application. The private level is constructed by replacing the black modules by specific textured patterns. It consists of information encoded using q-ary code with an error correction capacity. This allows us not only to increase the storage capacity of the QR code, but also to distinguish the original document from a copy. This authentication is due to the sensitivity of the used patterns to the print-and-scan (P&S) process. The pattern recognition method that we use to read the second-level information can be used both in a private message sharing and in an authentication scenario. It is based on maximizing the correlation values between P&S degraded patterns and reference patterns. The storage capacity can be significantly improved by increasing the code alphabet q or by increasing the textured pattern size. The experimental results show a perfect restoration of private information. It also highlights the possibility of using this new rich QR code for document authentication

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The authentication of printed documents is a nowadays challenge. One of the promising solutions for document authentication is the use of copy sensitive graphical codes that can offer data storage and support authentication. Both data decoding and physical authentication are based on comparison between printed-and-scanned samples and original numerical codes. In this paper we want to evaluate different existing correlation measures (Kendall and Spearman) and to propose a new Kendall weighted correlation metric. We propose to evaluate this new method by considering its ability to decode stored messages and to evaluate document authenticity.

The falsification of hardcopy documents is a common problem these days. Numerous industrial and scientific solutions have been proposed to prevent these falsifications. In this paper, we want to study the security of the two level QR code which is constructed using specific textured patterns that are sensitive to print-and-scan impact. Such code is a good candidate because it generalizes several concepts from several codes. We take a falsifier point of view that aims to reconstruct the two level QR code and to fool the authentication system detector. As the two level QR code contains sets of the same textured patterns, the opponent has access to different printed-and-scanned versions of these textured patterns. These sets of patterns can be used for structure estimation. Several local strategies for pattern estimation are suggest in this paper. The experimental results show that the increasing number of printed-and-scanned patterns cannot improve the estimation results.

Color noise-based feature for splicing detection and localization.Abstract-Images that have been altered and more specifically spliced together have invaded the digital domain due to the ease with which we are able to copy and paste them. To detect such forgeries the digital image processing community is proposing new automatic algorithms designed to help human operators reveal manipulated images. In this paper, we focus on a local detection system, which considers which tampered areas produce local statistical effects that do not impact neighboring areas or the image as a whole. We propose to study how the definition of local blocks, considering their size and overlap, impacts final pixel detection. We also propose new features which are an original way to consider the noise of an image as a colored signal. Indeed, in a non-forged image, there is a high correlation of noise between the three color channels R, G and B. We show that an optimal configuration can be defined and in this case the proposed approach outperforms several previously proposed methods using the same tested dataset, in uncompressed and JPEG modes. Note, in this paper we only focus on feature extraction without using machine learning.