The authors themselves concede that DW-GAN has wider uses:

Due to limited storage at GitHub, however, it is currently necessary to contact the lead author Ning Zhang in order to obtain the data associated with the framework.īecause DW-GAN has a ‘positive' mission for breaking CAPTCHAs (much as TOR itself originally had a positive mission for protecting military communications and, later, journalists), and because CAPTCHAs are both a legitimate defense (frequently and controversially used by ubiquitous CDN giant CloudFlare) and a favorite tool of illegitimate dark web marketplaces, the approach is arguably a ‘leveling' technology. Since the system – called Dark Web-GAN (DW-GAN, available at GitHub) – is apparently so much more performative than its predecessors, there is the possibility that it will be used as a general method to overcome the (usually less difficult) CAPTCHA material on the standard web, either in this specific implementation, or based on the general principles that the new paper outlines. The paper is titled Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence, and comes from researchers at the University of Arizona, the University of South Florida, and the University of Georgia. This could enable scalable and high-volume analyses, as well as the development of new cybersecurity approaches and techniques, which have been hamstrung, to date, by CAPTCHA firewalls. If the system can prove adaptable and resilient, it may further pave the way for more automated oversight systems, and for the indexing and web-scraping of TOR networks.

The authors contend that their approach represents a breakthrough for cybersecurity researchers, who traditionally have had to bear the costs of supplying humans-in-the-loop to manually solve CAPTCHAs, usually via crowdsourcing platforms such as Amazon Mechanical Turk (AMT).

Aio bot solving captcha