A Machine Learning Technique to Detect Counterfeit Medicine Based on X-Ray Fluorescence Analyser

Alsallal, Muna; Sharif, Mhd Saeed; Al-Ghzawi, Baydaa; Mutoki, Sabah Mohammed Mlkat al

doi:10.1109/iccecome.2018.8659110

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…In these studies, most of them study counterfeit currencies [25,26,[31][32][33] or medicines [27,28,[34][35][36], and a few investigate counterfeit luxury handbags [29,30]. For example, Desai et al [31] proposed a method combining CNN and Generative Adversarial Network (GAN) to detect counterfeit India currency.…”

Section: Counterfeit Detection Based On Cnnsmentioning

confidence: 99%

“…Inspired by these successful practices, some studies have begun to apply CNNs for the detection of counterfeit goods. Among these methods, their main research objects are counterfeit currencies [25,26], counterfeit medicines [27,28], and counterfeit luxury handbags [29,30]. By collecting a large number of specific product images and annotating their labels (i.e., real or counterfeit), existing CNN-based methods tend to directly use CNNs to learn the difference between the real and counterfeit classes and thereby realizing the detection of counterfeit products.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid attention network with appraiser-guided loss for counterfeit luxury handbag detection

Peng

Zou

et al. 2022

Complex Intell. Syst.

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Recently, convolutional neural networks have shown good performance in many counterfeit detection tasks. However, accurate counterfeit detection is still challenging due to the following three issues: (1) fine-grained classification, (2) class imbalance, and (3) high imitation samples. To address these issues, we propose a hybrid attention network (HANet) for counterfeit luxury handbag detection. In HANet, a hybrid attention module is first designed. Compared with existing methods that directly use classic CNNs for counterfeit detection, the HA module jointly uses a channel attention unit and a spatial attention unit to learn important information on both the channel and spatial dimensions. The HA modules can be easily integrated into the ResNet architecture to enhance the discriminative representation ability of CNNs, so as to help the network find subtle differences between the real and counterfeit products. In addition, an appraiser-guided loss is proposed to train HANet. Considering the factor of class imbalance and high imitation samples, the proposed loss gives the counterfeit class a higher weighting, and meanwhile gives the high imitation samples a much higher weighting. The proposed loss introduces the knowledge of appraisers, which allows HANet to not only treat real and counterfeit samples relatively fairly, but also pay more attention to the learning of difficult samples. To evaluate the performance of our method, we have constructed a well-benchmarked luxury handbag dataset. On this dataset, the performance of HANet, ResNet50, and the state-of-the-art attention methods is compared. The results demonstrate that HANet achieve superior performance against all its competitors.

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Section: Counterfeit Detection Based On Cnnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid attention network with appraiser-guided loss for counterfeit luxury handbag detection

Peng

Zou

et al. 2022

Complex Intell. Syst.

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“…The proposed system can also benefit different stakeholders in the pharmaceutical industry. For manufacturers, the system can provide greater visibility into the movement of their products and help to prevent the distribution of counterfeit drugs [25]. For distributors and pharmacists, the system can provide an easy way to verify the authenticity of medicines and ensure that they meet the required quality standards.…”

Section: Introductionmentioning

confidence: 99%

Investigating to detect the fake medicines using blockchain technology

Upadhyay

Bhargava

Kumar

2023

Preprint

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In recent years, the use of counterfeit drugs has become a significant public health concern globally. To tackle this problem, various technologies have been employed, including blockchain, due to its inherent characteristics such as transparency, immutability, and decentralization. In this paper,a method is proposed for detecting fake medicines using blockchain technology. The system tracks the entire lifecycle of a drug, from manufacturing todistribution, and maintains a decentralized ledger that records each transaction. This approach enables stakeholders to verify the authenticity of drugs inreal-time, ensuring that only genuine medicines reach patients. The system’seffectiveness in detecting counterfeit drugs is evaluated using a simulated environment, and the results show that the proposed method can effectively detectfake medicines with high accuracy and reliability. Overall, the use of blockchaintechnology in the detection of counterfeit drugs provides a promising solutionto the issue of fake medicines in the healthcare industry.

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