Cork as a Unique Object: Device, Method, and Evaluation

Abstract

Unique Objects (UNOs) are relevant for real-world applications such as anti-counterfeiting systems. In this work, cork is demonstrated as a UNO, part of the Physical Unclonability and Disorder (PUD) system. An adequate measurement kit (illumination device) and recognition method are also devised and evaluated. Natural hills and valleys of the cork are enhanced using the illumination device and the overall robustness of the recognition application inherent to UNOs is presented. The lighting device is based on grazing light and the recognition task is based on a local feature detector and descriptor called ORB - Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The performance evaluation utilizes a private cork database (1500 photos of 500 cork stoppers) and three public iris databases. In the tests carried out on the illumination device, the results clearly show the success of capturing stable/repeatable features needed for the recognition task in the cork database. This achievement is also reflected in the perfect recognition score achieved in the cork database, in the intra-distance measure μ i n t r a , which gives the notion of average noise between measures, and in the inter-distance μ i n t e r which provides hints about the randomness/uniqueness of a cork. Regarding the recognition application, its effectiveness is further tested using the iris databases. Regardless of the fact that the recognition algorithm was not designed for the iris recognition problem, the results show that the proposed approach is capable of competing with the techniques found in the literature specially designed for iris recognition. Furthermore, the evaluation shows that the three requirements that constitute a UNO (Disorder, Operability, and Unclonability) are fulfilled, thus supporting the main assertion of this work: that cork is a UNO.

Design and Comparison of Image Hashing Methods: A Case Study on Cork Stopper Unique Identification

“…The percentage of images for which the correct match falls within top 1, top 5, top and Top 10 (also called success rate) was obtained for each hash test set and for each hash function. The top 5 and top 10 results are of interest, given the expectation of higher success rates than those achieved by the top 1 research while also considerably reducing the search space, allowing for algorithms, such as the one presented by [ 10 ] to be applied in a timely manner. They display the values of the minimum success rates between both TS1 and TS2 when matched with the DB, for a specific case and method.…”

“…For evaluation purposes, a database (DB) [ 10 ] was used containing images taken with a phone camera (Sony Z3X1, Tokyo, Japan) and two test sets (TS1 and TS2) containing images taken with two other different cameras (Asus Zenfone 5,Taipei, Taiwan, and Sony PlayStation Eye, respectively). In these tests, each DB hash was compared to every hash in both TS1 and TS2, with the similarity between each hash pair being evaluated by the correspondent hamming distance (HD).…”

“…For this purpose, some methods have already been put into practice such as bar codes, Quick Response (QR) codes, Radio-Frequency IDentification (RFID) tags, Near Field Communication (NFC) technology, and even serial numbers, among others. However, they do not constitute fully functional options, not only because they can be easily manipulated and cloned but also because, in their vast majority, they identify the packaging instead of the product itself [ 8 , 9 , 10 ].…”

“…It has been proved that cork stoppers are unique objects [ 10 ] with characteristics that allow their classification as UNOs. Costa et al [ 15 ] suggested a method based on clustering and matching images with the Recognition of Individual Objects using Tagless Approaches (RIOTA) algorithm.…”

“…In this regard, it is important to refer to the concept of Physical Unclonability and Disorder (PUD). PUD [11] security systems use randomness in the distribution of certain physical characteristics of an object to identify it, and can be divided into two categories: UNique Objects [10] (UNOs) and Physical Unclonable Functions (PUFs) [10,12,13].…”