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Towards Image-Based Dark Vendor Profiling: An Analysis of Image Metadata and Image Hashing in Dark Web Marketplaces

Anonymity networks, such as Tor, facilitate the hosting of hidden online marketplaces where dark vendors are able to anonymously trade paraphernalia such as drugs, weapons, and hacking services. Effective dark marketplace analysis and dark vendor profiling techniques support dark web investigations and help to identify and locate these perpetrators. Existing automated techniques are text-based, leaving non-textual artifacts, such as images, out of consideration. Though image data can further improve investigative analysis, there are two primary challenges associated with dark web image analysis: (a) ethical concerns over the presence of child exploitation imagery in illegal markets, and (b) the computational overhead needed to download, analyze, and store image content. In this research, we investigate and address the aforementioned challenges to enable dark marketplace image analysis. Namely, we examine image metadata and explore several image hashing techniques to represent image content, allowing us to collect image-based intelligence and identify reused images among dark marketplaces while preventing exposure to illegal content and decreasing computational overhead. Our study reveals that approximately 75% of dark marketplace listings include image data, indicating the importance of considering image content for investigative analysis. Additionally, 2% of considered images were found to contain metadata and approximately 50% of image hashes were repeated among marketplace listings, suggesting the presence of easily obtainable incriminating evidence and frequency of image reuse among dark vendors. Finally, through an image hash analysis, we demonstrate the effectiveness of using image hashing to identify similar images between dark marketplaces.