Ricerc@Sapienza

To this date, CAPTCHAs have served as the first line of defense preventing unauthorized access by (malicious) bots to web-based services, while at the same time maintaining a trouble-free experience for human visitors. However, recent work in the literature has provided evidence of sophisticated bots that make use of advancements in machine learning (ML) to easily bypass existing CAPTCHA-based defenses. In this work, we take the first step to address this problem. We introduce CAPTURE, a novel CAPTCHA scheme based on adversarial examples.

Deep neural networks have had enormous impact on various domains of computer science applications, considerably outperforming previous state-of-the-art machine learning techniques. To achieve this performance, neural networks need large quantities of data and huge computational resources, which heavily increase their costs. The increased cost of building a good deep neural network model gives rise to a need for protecting this investment from potential copyright infringements.