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dStyle-GAN: Generative Adversarial Network based on Writing and Photography Styles for Drug Identification in Darknet Markets
Despite the persistent effort by law enforcement, illicit drug trafficking in darknet markets has shown great resilience with new markets rapidly appearing after old ones being shut down. In order to more effectively detect, disrupt and dismantle illicit drug trades, there's imminent need to gain a deeper understanding toward the operations and dynamics of illicit drug trading activities. To address this challenge, in this paper, we design and develop an intelligent system (named dSytle-GAN ) to automate the analysis for drug identification in darknet markets, by considering both content-based and style-aware information. To determine whether a given pair of posted drugs are the same or not, in dStyle-GAN, based on the large-scale data collected from darknet markets, we first present an attributed heterogeneous information network (AHIN) to depict drugs, vendors, texts and writing styles, photos and photography styles, and the rich relations among them; and then we propose a novel generative adversarial network (GAN) based model (named Drug-GAN) to capture the underlying distribution of posted drugs’ writing and photography styles to learn robust representations of drugs for their identifications. Unlike existing approaches, Drug-GAN jointly considers the heterogeneity of network and relatedness over drugs formulated by domain-specific meta-paths for robust node (i.e., drug) representation learning. To the best of our knowledge, the proposed dStyle-GAN represents the first principled GAN-based solution over graphs to simultaneously consider writing and photography styles as well as their latent distributions for node representation learning. Extensive experimental results based on large-scale datasets collected from six darknet markets and the obtained ground-truth demonstrate that dStyle-GAN outperforms the state-of-the-art methods. Based on the identified drug pairs in the wild by dStyle-GAN, we perform further analysis to gain deeper insights into the dynamics and evolution of illicit drug trading activities in darknet markets, whose findings may facilitate law enforcement for proactive interventions.