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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM42775

Survey Paper On “Fake Products Detection Using Machine Learning”

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM42775

Volume:

9 |

3 |

Mar 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MK

Mrs. Kokare.S.A1

HS

HOD. Shah.S.N2

ARS

Avate Rutuja Sunil3

RDM

Rokade Dnyaneshwari Mohan4

TSS

Taware Sakshi Sandip5

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Abstract

The proliferation of counterfeit products and fraudulent branding presents significant challenges for consumers and businesses alike. The Fake Product & Fake Logo Identification System aims to address these challenges by leveraging advanced machine learning and computer vision techniques to accurately identify counterfeit items and logos. This system employs a combination of image recognition, pattern analysis, and AI-driven algorithms to detect discrepancies between genuine and fake products by analyzing subtle differences in logos, packaging, and design elements. By utilizing a comprehensive database of authentic product images and logos, the system compares and flags anomalies indicative of counterfeiting. The goal is to provide a reliable, scalable, and user-friendly solution that empowers consumers and businesses to combat the adverse effects of counterfeit products, enhancing trust and safety in the marketplace.

KEYWORDS

Fake Product & Fake Logo Identification System counterfeit items and logos of image recognition pattern analysis and AI-driven algorithms

Cite This Article

Mrs. Kokare.S.A1, HOD. Shah.S.N2, Avate Rutuja Sunil3, Rokade Dnyaneshwari Mohan4, Taware Sakshi Sandip5, "Survey Paper On “Fake Products Detection Using Machine Learning”," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 3, Mar 2025.

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Publication Date

3/22/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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