The robustness of animated text CAPTCHAs

PhD ThesisCAPTCHA is standard security technology that uses AI techniques to tells computer and human apart. The most widely used CAPTCHA are text-based CAPTCHA schemes. The robustness and usability of these CAPTCHAs relies mainly on the segmentation resistance mechanism that provides robustness against individual character recognition attacks. However, many CAPTCHAs have been shown to have critical flaws caused by many exploitable invariants in their design, leaving only a few CAPTCHA schemes resistant to attacks, including ReCAPTCHA and the Wikipedia CAPTCHA. Therefore, new alternative approaches to add motion to the CAPTCHA are used to add another dimension to the character cracking algorithms by animating the distorted characters and the background, which are also supported by tracking resistance mechanisms that prevent the attacks from identifying the main answer through frame-toframe attacks. These technologies are used in many of the new CAPTCHA schemes including the Yahoo CAPTCHA, CAPTCHANIM, KillBot CAPTCHAs, non-standard CAPTCHA and NuCAPTCHA. Our first question: can the animated techniques included in the new CAPTCHA schemes provide the required level of robustness against the attacks? Our examination has shown many of the CAPTCHA schemes that use the animated features can be broken through tracking attacks including the CAPTCHA schemes that uses complicated tracking resistance mechanisms. The second question: can the segmentation resistance mechanism used in the latest standard text-based CAPTCHA schemes still provide the additional required level of resistance against attacks that are not present missed in animated schemes? Our test against the latest version of ReCAPTCHA and the Wikipedia CAPTCHA exposed vulnerability problems against the novel attacks mechanisms that achieved a high success rate against them. The third question: how much space is available to design an animated text-based CAPTCHA scheme that could provide a good balance between security and usability? We designed a new animated text-based CAPTCHA using guidelines we designed based on the results of our attacks on standard and animated text-based CAPTCHAs, and we then tested its security and usability to answer this question. ii In this thesis, we put forward different approaches to examining the robustness of animated text-based CAPTCHA schemes and other standard text-based CAPTCHA schemes against segmentation and tracking attacks. Our attacks included several methodologies that required thinking skills in order to distinguish the animated text from the other animated noises, including the text distorted by highly tracking resistance mechanisms that displayed them partially as animated segments and which looked similar to noises in other CAPTCHA schemes. These attacks also include novel attack mechanisms and other mechanisms that uses a recognition engine supported by attacking methods that exploit the identified invariants to recognise the connected characters at once. Our attacks also provided a guideline for animated text-based CAPTCHAs that could provide resistance to tracking and segmentation attacks which we designed and tested in terms of security and usability, as mentioned before. Our research also contributes towards providing a toolbox for breaking CAPTCHAs in addition to a list of robustness and usability issues in the current CAPTCHA design that can be used to provide a better understanding of how to design a more resistant CAPTCHA scheme

Using Synthetic Data to Train Neural Networks is Model-Based Reasoning

We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inference in the synthetic-data generative model. We demonstrate this connection in a recognition task where we develop a novel Captcha-breaking architecture and train it using synthetic data, demonstrating both state-of-the-art performance and a way of computing task-specific posterior uncertainty. Using a neural network trained this way, we also demonstrate successful breaking of real-world Captchas currently used by Facebook and Wikipedia. Reasoning from these empirical results and drawing connections with Bayesian modeling, we discuss the robustness of synthetic data results and suggest important considerations for ensuring good neural network generalization when training with synthetic data.Comment: 8 pages, 4 figure

Completely Automated Public Physical test to tell Computers and Humans Apart: A usability study on mobile devices

A very common approach adopted to fight the increasing sophistication and dangerousness of malware and hacking is to introduce more complex authentication mechanisms. This approach, however, introduces additional cognitive burdens for users and lowers the whole authentication mechanism acceptability to the point of making it unusable. On the contrary, what is really needed to fight the onslaught of automated attacks to users data and privacy is to first tell human and computers apart and then distinguish among humans to guarantee correct authentication. Such an approach is capable of completely thwarting any automated attempt to achieve unwarranted access while it allows keeping simple the mechanism dedicated to recognizing the legitimate user. This kind of approach is behind the concept of Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), yet CAPTCHA leverages cognitive capabilities, thus the increasing sophistication of computers calls for more and more difficult cognitive tasks that make them either very long to solve or very prone to false negatives. We argue that this problem can be overcome by substituting the cognitive component of CAPTCHA with a different property that programs cannot mimic: the physical nature. In past work we have introduced the Completely Automated Public Physical test to tell Computer and Humans Apart (CAPPCHA) as a way to enhance the PIN authentication method for mobile devices and we have provided a proof of concept implementation. Similarly to CAPTCHA, this mechanism can also be used to prevent automated programs from abusing online services. However, to evaluate the real efficacy of the proposed scheme, an extended empirical assessment of CAPPCHA is required as well as a comparison of CAPPCHA performance with the existing state of the art. To this aim, in this paper we carry out an extensive experimental study on both the performance and the usability of CAPPCHA involving a high number of physical users, and we provide comparisons of CAPPCHA with existing flavors of CAPTCHA

CAPTCHaStar! A novel CAPTCHA based on interactive shape discovery

Over the last years, most websites on which users can register (e.g., email providers and social networks) adopted CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) as a countermeasure against automated attacks. The battle of wits between designers and attackers of CAPTCHAs led to current ones being annoying and hard to solve for users, while still being vulnerable to automated attacks. In this paper, we propose CAPTCHaStar, a new image-based CAPTCHA that relies on user interaction. This novel CAPTCHA leverages the innate human ability to recognize shapes in a confused environment. We assess the effectiveness of our proposal for the two key aspects for CAPTCHAs, i.e., usability, and resiliency to automated attacks. In particular, we evaluated the usability, carrying out a thorough user study, and we tested the resiliency of our proposal against several types of automated attacks: traditional ones; designed ad-hoc for our proposal; and based on machine learning. Compared to the state of the art, our proposal is more user friendly (e.g., only some 35% of the users prefer current solutions, such as text-based CAPTCHAs) and more resilient to automated attacks.Comment: 15 page

Graffiti Networks: A Subversive, Internet-Scale File Sharing Model

The proliferation of peer-to-peer (P2P) file sharing protocols is due to their efficient and scalable methods for data dissemination to numerous users. But many of these networks have no provisions to provide users with long term access to files after the initial interest has diminished, nor are they able to guarantee protection for users from malicious clients that wish to implicate them in incriminating activities. As such, users may turn to supplementary measures for storing and transferring data in P2P systems. We present a new file sharing paradigm, called a Graffiti Network, which allows peers to harness the potentially unlimited storage of the Internet as a third-party intermediary. Our key contributions in this paper are (1) an overview of a distributed system based on this new threat model and (2) a measurement of its viability through a one-year deployment study using a popular web-publishing platform. The results of this experiment motivate a discussion about the challenges of mitigating this type of file sharing in a hostile network environment and how web site operators can protect their resources