Fake Product Detection Using Convolutional Neural Networks

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]09/09/2025 at 10:10 AM[/if 2224] | [if 1553 equals=””] Volume : 03 [else] Volume : 03[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02 | Page : 08 15

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    Venkatesh R., Moureeswaran S., Prasanna Venkatesh S., Ragul S.R., Shankar K.S.,

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  1. Assistant Professor, Student, Student, Student, Student, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Department of Computer Science, Karpagam College of Engineering, Coimbatore, Tamil Nadu, Tamil Nadu, Tamil Nadu, Tamil Nadu, Tamil Nadu, India, India, India, India, India

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Abstract

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nThe widespread circulation of counterfeit products in global markets presents a significant threat to both consumer trust and the integrity of established brands. With the advancement of artificial intelligence, particularly deep learning, there is growing potential to develop more sophisticated systems to combat this issue. This study introduces a novel counterfeit detection framework using the VGG16 Convolutional Neural Network (CNN) to distinguish between authentic and counterfeit products through image analysis. The approach involves processing visual data, such as product or logo images, and classifying them into genuine or fake categories. Utilizing the powerful feature extraction capabilities of the pre-trained VGG16 architecture, the model is fine-tuned with a specialized dataset containing labeled images of both authentic and counterfeit items. This allows the system to learn highly discriminative visual patterns associated with each class. Experimental results demonstrate the effectiveness of the proposed framework, showcasing high accuracy and robustness in detecting fraudulent goods. The model successfully captures subtle visual cues that are often overlooked by the human eye or traditional inspection methods. Ultimately, this research provides a practical and scalable solution for enhancing product verification processes, thereby reinforcing brand protection strategies and promoting consumer safety in an increasingly complex and deceptive commercial landscape.nn

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Keywords: Fake product detection, VGG16, convolutional neural network (CNN), image classification, product authentication, deep learning, counterfeit detection

n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Algorithms Design and Analysis Review ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in International Journal of Algorithms Design and Analysis Review (ijadar)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nVenkatesh R., Moureeswaran S., Prasanna Venkatesh S., Ragul S.R., Shankar K.S.. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Fake Product Detection Using Convolutional Neural Networks[/if 2584]. International Journal of Algorithms Design and Analysis Review. 08/09/2025; 03(02):08-15.

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nVenkatesh R., Moureeswaran S., Prasanna Venkatesh S., Ragul S.R., Shankar K.S.. [if 2584 equals=”][226 striphtml=1][else]Fake Product Detection Using Convolutional Neural Networks[/if 2584]. International Journal of Algorithms Design and Analysis Review. 08/09/2025; 03(02):08-15. Available from: https://journals.stmjournals.com/ijadar/article=08/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Original Research

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Volume 03
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02
Received 22/03/2025
Accepted 09/05/2025
Published 08/09/2025
Retracted
Publication Time 170 Days

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