# Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier
## 5 Discussion  
At present, counterfeit and inferior products cause economic losses to consumers and disrupt the normal economic order of society. The anti-counterfeiting label printed or pasted on the package can prevent anti-counterfeiting to some extent, but some limitations lead to a low query rate of identification by customers.  
Many existing schemes rely on special equipment such as scanners or microscopes, and customers have to go to a particular place for the query service. In other cases, the scheme allows customers to take a photo of the label and upload the image to achieve an identification query, but the ability to authenticate a blurry pattern image is weak, leading to a low recognition rate at mobile terminals. It may take several tries for a customer to take a photo that is clear enough for a query.  
According to the literature [41], a binary classification method may perform better for anti-counterfeiting pattern identification compared to a one-class method. However, it is difficult to collect enough counterfeit patterns during the design process in practical applications. The existing binary classification methods based on deep learning rely on a large amount of training data containing both positive and negative samples, while collecting the negative samples can increase the cost. Considering the fact that there are far more authentic products than fakes, it is necessary to apply one-class based anti-counterfeiting methods in a practical scenario.  
Therefore, an authentication scheme for an anti-counterfeiting pattern captured by a smartphone is proposed in this paper, and a two-stage single classifier is designed in the scheme. The classifier only needs to learn positive samples to achieve effective identification. In the first stage, the U-Net-based auto-encoder is trained with artificially blurred pattern images for feature extraction, and artificially blurred images are used for training to enhance the robustness of restoration ability. In the second stage, the features extracted by U-Net are used for one-class classification. Based on the standard OCSVM method, the generated boundary is modified according to certain strategies so that more positive samples fall within the decision boundary, and the recognition rate for the blurry image of the genuine pattern is improved. Compared with the traditional method, with the same number of samples, the proposed method achieves a recognition rate of 100% for authentic products with certain fuzziness, and the false classification rate decreases to 0%, which verifies the effectiveness of the proposed method.  
Compared with multi-class classification approaches, there are few methods for deep one-class classification. In this work, the mostly-used two-stage framework for deep one-class classification is applied. First, a deep learning model is trained for feature extraction, and the extracted feature is used to train a classification model. Recently, some deep end-to-end methods for one-class classification are proposed, where the feature extraction model and the classification model can be trained together with a shared loss function. In our future work, a deep end-to-end model for one-class classification can be considered as a substitute for the two-stage framework model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **[PERSON]’s** & **[PERSON]’s** & **Proposed** \\ \hline TP rate & 93.9 & 50.4 & **100** \\ FP rate & 15.6 & 0 & **0** \\ Precision & 85.7 & 100 & 100 \\ Recall & 93.9 & 50.4 & **100** \\ F\({}_{1}\) score & 89.6 & 67.0 & **100** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of deep one-class methods (%).