A two-stage deep learning framework for counterfeit luxury handbag detection in logo images

Peng, Jianbiao; Zou, Beiji; Zhu, Chengzhang

doi:10.1007/s11760-022-02352-7

SIViP

2022

DOI: 10.1007/s11760-022-02352-7

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A two-stage deep learning framework for counterfeit luxury handbag detection in logo images

Jianbiao Peng

Beiji Zou

Chengzhang Zhu

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2024

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Cited by 2 publications

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A lightweight multi-task learning network based on key area guidance for counterfeit detection

Yang,

Zhou

et al. 2024

SIViP

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Counterfeit detection traditionally relies on manual efforts, but manual detection efficiency is notably low. The accuracy of deep learning methods is challenging because of the insufficient samples, so it is crucial to allow the model to learn effective representation at a lower training cost. Given the above problems, we proposed a lightweight multi-task learning method that employs an uncomplicated auxiliary task to enhance the main task’s attention and reduce the training sample requirements. A key area guidance algorithm is designed to construct the auxiliary task, disturbing key image areas to generate new samples and training the auxiliary task to recognize the disturbance. This guides the main task in discerning authenticity from these key areas. Additionally, a tailored data preprocessing strategy was designed to improve the method’s performance further. Achieving an impressive 98.8% accuracy in identifying various counterfeiting points, our method outperforms existing advanced methods. Importantly, the method significantly reduces training costs. Even with an 80% reduction in the sample size, the method maintains a 92.1% accuracy, demonstrating minimal performance degradation compared to alternative methods.

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A lightweight multi-task learning network based on key area guidance for counterfeit detection

Yang,

Zhou

et al. 2024

SIViP

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Deep Learning based Counterfeit Nike Shoes Detection using YOLOv8 for Object Detection

Panchal,

Vora

2024

JIIP

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The fashion and footwear industries, where brand value and customer trust are paramount, are under constant threat from counterfeit products. This research presents a deep learning-based solution for detecting counterfeit Nike shoes using the YOLOv8 model for object detection. the dataset included four classes: “Nike Fake Air Force,” “Nike Fake Jordan 1,” “Nike Original Air Force,” and “Nike Original Jordan 1,” comprising a total of 3,860 images. These were split into training (70%), validation (20%), and testing (10%) sets. The pre-trained, medium-sized YOLOv8 model was used for detection and classification, yielding promising results. The model achieved a mAP of 95.0%, with precision and recall scores of 92.2% and 91.8%, respectively, on the validation set. The images were web scrapped with the Chrome extension called "Download All Images" and then manually filtered so that they would be relevant and of good quality. Each image was then manually labelled using the RoboFlow platform. Nevertheless, the model appears to be promising for implementation in combating counterfeit products, with a high potential accuracy and efficiency.

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A two-stage deep learning framework for counterfeit luxury handbag detection in logo images

Cited by 2 publications

References 32 publications

A lightweight multi-task learning network based on key area guidance for counterfeit detection

A lightweight multi-task learning network based on key area guidance for counterfeit detection

Deep Learning based Counterfeit Nike Shoes Detection using YOLOv8 for Object Detection

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