Counterfeit Product Detection Using Structured Prediction-Based Deep Convolutional Generative Adversarial Networks for Classification

For many different industries, the proliferation of counterfeit goods now poses a significant challenge that compromises brand integrity and consumer confidence in them. Conventional methods of spotting counterfeit products sometimes depend on hand inspection or basic image recognition algorithms. These methods rely on hand inspection, thus they could not be able to find minute differences between actual and fake products. A solution that is not only automated but also robust and scalable is quite important to detect false products in real time. On the other hand, traditional deep learning techniques are not able to incorporate structural dependencies in image data, which might help to raise the accuracy of counterfeit detection. Aiming for counterfeit product classification, this work offers a fresh framework based on Structured Prediction using Deep Convolutional Generative Adversarial Networks (DCGANs). The structured prediction inclusion of the model into the generative adversarial training process enables the network to better grasp interactions among many product features. There is a generator creating images like counterfeits and discriminator determining whether the images are real or copied in the architecture. Moreover integrated into the architecture are structured loss systems guiding training. The proposed approach outperforms traditional DCGANs and standard classifiers according to the experimental results obtained on a benchmark dataset including counterfeit products. The proposed method obtained rather remarkable results with a precision of 91.3 %, accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 2. 5 \%}$</tex>, recall of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 0. 7 \%}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F 1}$</tex>-score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 1 \%}$</tex>. Stressing small differences between the two helps the structured prediction mechanism greatly increase the model's ability to discriminate between real and fake products.