Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier

Abstract

At present, anti-counterfeiting schemes based on the combination of anti-counterfeiting patterns and two-dimensional codes is a research hotspot in digital anti-counterfeiting technology. However, many existing identification schemes rely on special equipment such as scanners and microscopes; there are few methods for authentication that use smartphones. In particular, the ability to classify blurry pattern images is weak, leading to a low recognition rate when using mobile terminals. In addition, the existing methods need a sufficient number of counterfeit patterns for model training, which is difficult to acquire in practical scenarios. Therefore, an authentication scheme for an anti-counterfeiting pattern captured by smartphones is proposed in this paper, featuring a single classifier consisting of two modules. A feature extraction module based on U-Net extracts the features of the input images; then, the extracted feature is input to a one-class classification module. The second stage features a boundary-optimized OCSVM classification method. The classifier only needs to learn positive samples to achieve effective identification. The experimental results show that the proposed approach has a better ability to distinguish the genuine and counterfeit anti-counterfeiting pattern images. The precision and recall rate of the approach reach 100%, and the recognition rate for the blurry images of the genuine anti-counterfeiting patterns is significantly improved.

Intelligent Materials and Nanomaterials Improving Physical Properties and Control Oriented on Electronic Implementations

“…Possible research developments are the application of AI in the detection of biological conditions for risk detection, finding possible nano-additives ensuring food security, and designing flexible electronic components. The research in alarm and security systems includes different topics such as the integration of nanomaterials with semi-conductor technology in alarm systems [251], image surveillance by means of miniaturized image vision devices [252][253][254][255], metasurfaces controlling information [256,257], shape memory alloys (SMA) improving impact damage tolerance in civil and military applications [258], and nanotechnology for anti-counterfeiting [259][260][261][262][263][264][265][266][267][268]. Embedded systems are constituted by all the elements detecting the alarm condition, including the materials, circuits, AI control, and alerting/risk processes to be executed.…”

Visible Frame QR-Coded Microparticles Integrated with an Invisible Layer for Unlocking Hidden Data via Fluorescence

“…Nonetheless, complete prevention of counterfeiting remains elusive. 20 The innovation for overcoming the challenges comes from a dual-layer approach that dynamically controls hidden data for security contexts through UV light exposure including visible information on dot-shaped QR code modules for everyday interactions. This dual functionality not only makes QR codes more resistant to tampering and counterfeiting, but can also help securely share data in highly confidential areas.…”

“…By adopting dot-shaped QR code modules, high decodability can be ensured without the need for fluorescent materials, while maintaining structural stability within the particles. Nonetheless, complete prevention of counterfeiting remains elusive …”