Fake Product Detection Using Convolutional Neural Networks

Year : 2025 | Volume : 03 | Issue : 02 | Page : 08 15
    By

    Venkatesh R.,

  • Moureeswaran S.,

  • Prasanna Venkatesh S.,

  • Ragul S.R.,

  • Shankar K.S.,

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

Abstract

The 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.

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

[This article belongs to International Journal of Algorithms Design and Analysis Review ]

How to cite this article:
Venkatesh R., Moureeswaran S., Prasanna Venkatesh S., Ragul S.R., Shankar K.S.. Fake Product Detection Using Convolutional Neural Networks. International Journal of Algorithms Design and Analysis Review. 2025; 03(02):08-15.
How to cite this URL:
Venkatesh R., Moureeswaran S., Prasanna Venkatesh S., Ragul S.R., Shankar K.S.. Fake Product Detection Using Convolutional Neural Networks. International Journal of Algorithms Design and Analysis Review. 2025; 03(02):08-15. Available from: https://journals.stmjournals.com/ijadar/article=2025/view=225913


References

  1. Amankeldin D, Kurmangaziyeva L, Mailybayeva A, Glazyrina N, Zhumadillayeva A, Karasheva N. Deep Neural Network for Detecting Fake Profiles in Social Networks. Comput Syst Sci Eng. 2023 Oct 1; 47(1): 1091–1108.
  2. Cheung M, She J, Liu L. Deep learning-based online counterfeit-seller detection. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). 2018 Apr 15; 51–56.
  3. Daoud E, Vu D, Nguyen H, Gaedke M. Enhancing fake product detection using deep learning object detection models. IADIS Int J Comput Sci Inf Syst. 2020 Jan 1; 15(1): 13–24.
  4. Asadizanjani N, Tehranipoor M, Forte D. Counterfeit electronics detection using image processing and machine learning. In: IOP Publishing: J Phys: Conf Ser. 2017; 787(1): 012023.
  5. Gayialis SP, Kechagias EP, Papadopoulos GA, Masouras D. A review and classification framework of traceability approaches for identifying product supply chain counterfeiting. Sustainability. 2022 May 30; 14(11): 6666.
  6. Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, PMLR. 2019 May 24; 6105–6114.
  7. Lee SH, Lee HY. Detecting counterfeit bills and their forgery devices using CNN-based deep learning. In Proc 13th Int Multi-Conf Comput Global Inf Technol. 2018; 16–20.
  8. Shukla S, Shukla N. Smart waste collection system based on IoT (Internet of Things): a survey. Int J Comput Appl. 2017 Mar; 162(3): 42–4.
  9. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. 2014 Sep 4.
  10. Rodrigues JE, Bezerra DM, Maciel AP, Paschoal AR, Paschoal CW. Ba (zn1/3nb2/3) o3 thin films obtained by polymeric precursors method. arXiv preprint arXiv:1212.2272. 2012 Dec 11.
Regular Issue Subscription Original Research
Volume 03
Issue 02
Received 22/03/2025
Accepted 09/05/2025
Published 08/09/2025
Publication Time 170 Days
162

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