## 2 Related Works
[15] proposed an individual object recognition system based on the surface patterns of Printed Circuit Boards (PCBs) for traceability purposes. The authors take advantage of the fiducial markers, commonly used to align the PCBs, to define the region of interest and capture the images. To find a match in their database, a template matching technique is employed. The results show that the proposed approach can accurately perform individual object recognition. All of these applications share some important assumptions: the object's intrinsic visual features are unique between objects of the same class, and as a result of its nature (e.g., manufacturing process, features from natural objects) it would be almost impossible to clone/replicate. Therefore, it will most likely allow safe individual object recognition. Either by stimulating the object and reading its response (e.g., paper authenticity check) or simply by reading its visual features (e.g., FIBAR [10], RIOTA [1]), the assumptions remain valid. As aforementioned, these kinds of applications are part of a larger concept named Physical Unclonability and Disorder and are divided in two types: UNOs and PUFs. There are several examples of UNO applications, like bank cards, passports or access cards, paper fibbers in banknotes, among others. The UNO application can be achieved in two ways [5]: the classic way, where a UNO is applied to the product to be protected, or certain features of the object are measured. With this measurement serving as the fingerprint, it is compared with another previously stored in a database. The alternative method is an extension of the previous one, with the addition of more information, such as a barcode. Therefore, the validation/authentication of a product involves two steps: (i) Read the additional information of the product and check its digital signature by using the public key stored on the device; (ii) get the fingerprint of the product and check if all digital signature and additional information is correct. Common attacks to UNOs are cloning or re-fabrication. A perfect reproduction from the original is not needed to bypass the measurement system. Indeed, the second structure just has to provide the same measured response as the original one. Theoretically, the replica could have a different size, lengthscale, or appearance. In addition, the clone could be a smart reactive device that artificially presents the same response. An Alternative to UNOs are Physical Unclonable Functions. These functions were introduced in 2001 by [PERSON] [16] and originally were called \"Physical One-Way Functions\". By definition, a PUF is a physical system \(S\) questioned by a challenge \(C\) that generates a unique response called \(R\). An applied challenge and its measured response is usually referred as Challenge-Response Pair (CRP) [17]. The generated response depends on the applied function and the disorder of the physical object. The unclonable demand on a PUF should be impossible for someone with access to the physical system to replicate the physical object or the software. In a typical application scenario, a PUF has two distinct phases: (i) enrollment phase--a number of CRPs are collected from one PUF and stored in a database; and (ii) verification phase--a challenge from the database is applied to the PUF and the response produced is compared with the corresponding response from the database. A typical PUF possesses five important characteristics [18]:  
* **Unique--**The output of a PUF is unpredictable as a result of the unique micro-structural variations. * **Unclonable--**Because of its inherent physical properties, cloning a PUF should be unfeasible or extremely difficult. Two PUFs cannot produce the same response via cloning. * **Unpredictable**--Given a set of known challenges \(C=(c_{1},c_{2},...,c_{n})\) producing the responses \(R=(r_{1},r_{2},...,r_{n})\), it should not be possible to predict the correct response \(r_{n+1}\) of the challenge \(c_{n+1}\).