# Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
It combines the physical anti-counterfeiting label, information technology, communication technology, big data and cloud platform, geographic information positioning and other advanced technical means to obtain the relevant information uploaded when the consumer scans the anti-counterfeiting label. In combination with the merchant information, commodity information and anti-counterfeiting identification information saved in the big data cloud platform, real-time matching and comparison are carried out so as to achieve anti-counterfeiting identification. When a serialized and unique barcode is printed on a product, it remains unchanged in the product's life cycle. However, with the development of information technology, more and more high-precision digital input and output devices have come out and become popular, making the printed two-dimensional code vulnerable to illegal copying: that is, the scanned and copied two-dimensional code can still obtain the same information as the original two-dimensional code after decoding [22; 23]. In recent years, the anti-counterfeiting scheme based on a printable graphic code (PGC) has low cost and strong anti-counterfeiting ability, which has attracted more and more attention in industry and academia. The copy detection pattern (CDP) is named for its sensitivity to illegal copying, which is also known as an anti-counterfeiting pattern [24; 25]. It is usually composed of a fine micro-texture. Compared with the original pattern, the pattern generated by illegal copying will have great changes in the texture micro-features. By capturing these micro-features through machine learning, CDP can be authenticated. In terms of applying anti-counterfeiting patterns for print matter anti-counterfeiting, many researchers have already put forward corresponding schemes. [PERSON] et al. [26; 27] proposed a two-level QR code (2 LQR code); unlike standard QR codes, 2 LQR codes use specific texture patterns instead of black-and-white block structures to construct a private level for private information or authentication. The patterns are sensitive to the print-and-scan (P&S) process, thus preventing copying. [PERSON] et al. [28] proposed an LCAC (low-cost anti-copying) 2D barcode, the anti-duplication effect of which is achieved by adding location-confidential authentication information to the original information in the process of generating a QR code. [PERSON] [29] designed an anti-counterfeiting QR code with a copy detection pattern, which is composed of small black and white blocks of different densities. The copy detection pattern is inserted into the digital image of the QR code before production and printing. The pattern is sensitive to the copy process and can be scanned and analyzed by a mobile app to identify forgery. [PERSON] [30] combined visual features with QR codes to design the anti-counterfeiting system of the Internet of Things. The visual features used included natural texture features and printed micro-features. In the anti-counterfeiting verification process, a feature extraction algorithm was used to calculate the feature similarity between the sample to be tested and the sample in the database to obtain identification results. According to the existing research, the authenticity identification method based on CDP still has the following problems or defects:  
1. At present, the commonly used method of authenticity identification is based on traditional machine learning, which usually relies on expert experience to manually design feature extraction and feature selection methods. In the design process, it is necessary to collect a large number of images of genuine anti-counterfeiting patterns and forged anti-counterfeiting patterns, extract and compare different features, and select one or several features with the best differentiation. However, traditional features are still surface features with limited representation ability. It is particularly difficult to design traditional manual features with robustness and good distinguishing ability for various sample changes.