Fine-grained classification of fashion items is essential for enhancing user experiences in online shopping, enabling more effective categorization and personalized recommendations. While recent advances in Artificial Intelligence (AI) have shown promise, achieving high classification accuracy typically requires large volumes of labeled data—an expensive and labor-intensive requirement, particularly burdensome for smaller retailers. Existing approaches attempt to mitigate this challenge by pretraining models on synthetic or geometric data and fine-tuning on limited fashion images, but these methods have thus far plateaued at around 90% accuracy, falling short of practical deployment standards. In this paper, we introduce a novel iterative image generation framework designed to overcome data scarcity and significantly improve classification accuracy. Our method combines a conditional Generative Adversarial Network (cGAN) with a ResNet50-based image classifier in a closed-loop system. The cGAN generates synthetic fashion images conditioned on class labels, while the classifier filters outputs based on confidence scores and predefined criteria. This cycle is repeated iteratively, progressively enriching the training dataset with high-quality synthetic images and refining the classifier. We validate our approach on a task involving classification of five distinct neckline types. The converged model achieves an average accuracy of 94.6%, substantially outperforming previous methods despite using a limited amount of real labeled data. These results highlight the effectiveness of iterative data augmentation using generative models for fine-grained visual classification in resource-constrained settings.

With the rapid expansion of wireless infrastructure and smart devices, short video platforms have become a staple of modern digital ecosystems, offering convenience but also introducing new risks. One such risk is illicit promotional content (IPC), which encompasses deceptive or fraudulent material intended to promote products, services, or events in violation of platform policies. As these platforms grow in popularity, so too does the threat of IPC, which has adapted to the short video format, referred to here as short video-illicit promotional content (SV-IPC). The detection of SV-IPC is crucial to protect users, especially minors, from fraudulent schemes and harmful material. Current detection approaches primarily rely on image processing, text analysis, and quick response (QR) code detection, limiting their effectiveness on short video platforms. This paper provides a comprehensive investigation into SV-IPC and its evasion techniques, revealing the underlying ecosystem of illicit promotion. To address these challenges, we introduce a hybrid detection framework that integrates natural language processing with video analysis. Extensive experiments conducted on Chinese TikTok validate the proposed scheme, demonstrating high effectiveness with an F1-score of 90.7%, recall of 90.3%, and precision of 91.2%. This study underscores the broader societal implications of SV-IPC and the importance of enhanced detection mechanisms.