Fake Food Product Detection Using Block Chain Technology

Abstract

In the contemporary global market landscape, the widespread existence of 
counterfeit merchandise poses a substantial menace to both consumers and 
legitimate enterprises. The emergence of fraudulent products not only erodes 
consumer confidence but also detrimentally affects the market presence and 
integrity of authentic brands. This paper presents an innovative strategy aimed at 
countering the proliferation of counterfeit goods by harnessing the power of block 
chain-based methodologies and the Salesman Algorithm. Our proposed approach 
is designed to identify and flag counterfeit items within the market by scrutinizing 
diverse data metrics and trends associated with their distribution and sales. 
Through the utilization of block chain-based techniques such as classification and 
clustering algorithms, the system can assimilate insights from past data and 
pinpoint irregularities indicative of counterfeit products. Moreover, we employ the 
Salesman Algorithm renowned for its optimization prowess to refine the inspection 
and surveillance procedures, thereby enhancing the efficacy of counterfeit 
detection endeavors. By seamlessly integrating block chain-based with the 
Salesman Algorithm, our methodology presents a holistic solution to tackle the 
challenges posed by counterfeit products in the market. By accurately discerning 
counterfeit merchandise, enterprises can safeguard their brand credibility, shield 
consumers from fraudulent transactions, and foster a more transparent and 
reliable marketplace. The efficacy of our proposed approach is validated through 
rigorous experimentation and analysis using authentic datasets, underscoring its 
potential to mitigate the detrimental impacts of counterfeit goods on the economy 
and society at large.