Abstract: A counterfeit detection system provides an artificial intelligence (AI) platform that implements a Generative Adversarial Network (GAN) to classify an image as one of a fake or genuine item and integrates a Classification Activation Module (CAM) to refine counterfeit detection. The GAN may include a generator that generates simulated counterfeit images for a discriminator. The discriminator may be trained to identify faked items by learning from the simulated counterfeit images and/or images of actual faked items. The discriminator may implement a deep neural network of convolutional layers that each analyze a region of an image and produce a weighted output that contributes to the classification based on the analyzed region. The CAM may identify the regions and weights relied upon by the discriminator, provide corresponding heatmaps to subject matter experts, receive annotations from the subject matter experts, and use the annotations as feedback to refine the classifier of the discriminator.

Abstract: A counterfeit detection system provides an artificial intelligence (AI) platform that implements a Generative Adversarial Network (GAN) to classify an image as one of a fake or genuine item and integrates a Classification Activation Module (CAM) to refine counterfeit detection. The GAN may include a generator that generates simulated counterfeit images for a discriminator. The discriminator may be trained to identify faked items by learning from the simulated counterfeit images and/or images of actual faked items. The discriminator may implement a deep neural network of convolutional layers that each analyze a region of an image and produce a weighted output that contributes to the classification based on the analyzed region. The CAM may identify the regions and weights relied upon by the discriminator, provide corresponding heatmaps to subject matter experts, receive annotations from the subject matter experts, and use the annotations as feedback to refine the classifier of the discriminator.