Fusing Multiscale Texture and Residual Descriptors for Multilevel 2D Barcode Rebroadcasting Detection (11:10 AM – 11:30 AM)
Anselmo Ferreira (University of Siena), Changsheng Chen (Shenzhen University) and Mauro Barni (University of Siena) – On-site presentation
Nowadays, 2D barcodes have been widely used for advertisement, mobile payment, and product authentication. However, in applications related to product authentication, an authentic 2D barcode can be illegally copied and attached to a counterfeited product in such a way to bypass the authentication scheme. In this paper, we employ a proprietary 2D barcode pattern and use multimedia forensics methods to analyse the scanning and printing artefacts resulting from the copy (rebroadcasting) attack. A diverse and complementary feature set is proposed to quantify the barcode texture distortions introduced during the illegal copying process. The proposed features are composed of global and local descriptors, which characterize the multiscale texture appearance and the points of interest distribution, respectively. The proposed descriptors are compared against some existing texture descriptors and deep learning-based approaches under various scenarios, such as cross-datasets and cross-size. Experimental results highlight the practicality of the proposed method in real-world settings.
Mobile authentication of copy detection patterns: how critical is to know fakes? (11:30 AM – 11:50 AM)
Olga Taran (Geneva University), Joakim Tutt (University of Geneva), Taras Holotyak (Geneva University), Roman Chaban (University of Geneva), Slavi Bonev (University of Geneva) and Slava Voloshynovskiy (University of Geneva) – Virtual presentation
Protection of physical objects against counterfeiting is an important task for the modern economies. In recent years, the high-quality counterfeits appear to be closer to originals thanks to the rapid advancement of digital technologies. To combat these counterfeits, an anti-counterfeiting technology based on hand-crafted randomness implemented in a form of copy detection patterns (CDP) is proposed enabling a link between the physical and digital worlds and being used in various brand protection applications. The modern mobile phone technologies make the verification process of CDP easier and available to the end customers. Besides a big interest and attractiveness, the CDP authentication based on the mobile phone imaging remains insufficiently studied. In this respect, in this paper we aim at investigating the CDP authentication under the real-life conditions with the codes printed on an industrial printer and enrolled via a modern mobile phone under the regular light conditions. The authentication aspects of the obtained CDP are investigated with respect to the four types of copy fakes. The impact of fakes’ type used for training of authentication classifier is studied in two scenarios: (i) supervised binary classification under various assumptions about the fakes and (ii) one-class classification under unknown fakes. The obtained results show that the modern machine-learning approaches and the technical capacity of modern mobile phones allow to make the CDP authentication under unknown fakes feasible with respect to the considered types of fakes and code design.
Machine learning attack on copy detection patterns: are 1×1 patterns cloneable? (11:50 AM – 12:10 PM)
Roman Chaban (University of Geneva), Olga Taran (Geneva University), Joakim Tutt (University of Geneva), Taras Holotyak (Geneva University), Slavi Bonev (University of Geneva) and Slava Voloshynovskiy (University of Geneva) – On-site presentation
Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market. Copy detection patterns (CDP) are considered as such solution in several applications. It is assumed that being printed at the maximum achievable limit of a printing resolution of an industrial printer with the smallest symbol size 1×1 elements, the CDP cannot be copied with sufficient accuracy and thus are unclonable. In this paper, we challenge this hypothesis and consider a copy attack against the CDP based on machine learning. The experimental based on samples produced on two industrial printers demonstrate that simple detection metrics used in the CDP authentication cannot reliably distinguish the original CDP from their fakes. Thus, the paper calls for a need of careful reconsideration of CDP cloneability and search for new authentication techniques and CDP optimization because of the current attack.
Can Copy Detection Patterns be copied? Evaluating the performance of attacks and highlighting the role of the detector (12:10 PM – 12:30 PM)
Elyes Khermaza (Scantrust), Iuliia Tkachenko (LIRIS) and Justin Picard (Scantrust) – On-site presentation
Copy Detection Patterns (CDP) have received significant attention from academia and industry as a practical mean of detecting counterfeits. Their security level against sophisticated attacks has been studied theoretically and practically in different research papers, but for reasons that will be explained below, the results are not fully conclusive. In addition, the publicly available CDP datasets are not practically usable to evaluate the performance of authentication algorithms. In short, the apparently simple question: “are copy detection patterns secure against copy?”, remains unanswered as of today. The primary contribution of this paper is to present a publicly available dataset of CDPs including multiple types of copies and attacks, allowing to systematically compare the performance level of CDPs against different attacks proposed in the prior art. The specific case in which a CDP is the same for an entire batch of prints, which is of practical importance as it covers applications with widely used industrial printers such as offset, flexo and rotogravure, is also studied. A second contribution is to highlight the role played by the CDP detector and its different processing steps. Indeed, depending on the specific processing involved, the detection performance can widely outperform the CDP bit error rate which has been used as a reference metrics in the prior art.