Gotta CAPTCHA ’Em All: A Survey of 20 Years of the Human-or-computer Dilemma

A recent study has found that malicious bots generated nearly a quarter of overall website traffic in 2019 [102]. These malicious bots perform activities such as price and content scraping, account creation and takeover, credit card fraud, denial of service, and so on. Thus, they represent a serious threat to all businesses in general, but are especially troublesome for e-commerce, travel, and financial services. One of the most common defense mechanisms against bots abusing online services is the introduction of Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), so it is extremely important to understand which CAPTCHA schemes have been designed and their actual effectiveness against the ever-evolving bots. To this end, this work provides an overview of the current state-of-the-art in the field of CAPTCHA schemes and defines a new classification that includes all the emerging schemes. In addition, for each identified CAPTCHA category, the most successful attack methods are summarized by also describing how CAPTCHA schemes evolved to resist bot attacks, and discussing the limitations of different CAPTCHA schemes from the security, usability, and compatibility point of view. Finally, an assessment of the open issues, challenges, and opportunities for further study is provided, paving the road toward the design of the next-generation secure and user-friendly CAPTCHA schemes.

Compressing atmospheric data into its real information content

Hundreds of petabytes are produced annually at weather and climate forecast centers worldwide. Compression is essential to reduce storage and to facilitate data sharing. Current techniques do not distinguish the real from the false information in data, leaving the level of meaningful precision unassessed. Here we define the bitwise real information content from information theory for the Copernicus Atmospheric Monitoring Service (CAMS). Most variables contain fewer than 7 bits of real information per value and are highly compressible due to spatio-temporal correlation. Rounding bits without real information to zero facilitates lossless compression algorithms and encodes the uncertainty within the data itself. All CAMS data are 17× compressed relative to 64-bit floats, while preserving 99% of real information. Combined with four-dimensional compression, factors beyond 60× are achieved. A data compression Turing test is proposed to optimize compressibility while minimizing information loss for the end use of weather and climate forecast data.

The Modified Imitation Game: A Method for Measuring Interactional Expertise

The study of the sociology of scientific knowledge distinguishes between contributory and interactional experts. Contributory experts have practical expertise—they can “walk the walk.” Interactional experts have internalized the tacit components of expertise—they can “talk the talk” but are not able to reliably “walk the walk.” Interactional expertise permits effective communication between contributory experts and others (e.g., laypeople), which in turn facilitates working jointly toward shared goals. Interactional expertise is attained through long-term immersion into the expert community in question. To assess interactional expertise, researchers developed the imitation game—a variant of the Turing test—to test whether a person, or a particular group, possesses interactional expertise of another. The imitation game, which has been used mainly in sociology to study the social nature of knowledge, may also be a useful tool for researchers who focus on cognitive aspects of expertise. In this paper, we introduce a modified version of the imitation game and apply it to examine interactional expertise in the context of blindness. Specifically, we examined blind and sighted individuals’ ability to imitate each other in a street-crossing scenario. In Phase I, blind and sighted individuals provided verbal reports of their thought processes associated with crossing a street—once while imitating the other group (i.e., as a pretender) and once responding genuinely (i.e., as a non-pretender). In Phase II, transcriptions of the reports were judged as either genuine or imitated responses by a different set of blind and sighted participants, who also provided the reasoning for their decisions. The judges comprised blind individuals, sighted orientation-and-mobility specialists, and sighted individuals with infrequent socialization with blind individuals. Decision data were analyzed using probit mixed models for signal-detection-theory indices. Reasoning data were analyzed using natural-language-processing (NLP) techniques. The results revealed evidence that interactional expertise (i.e., relevant tacit knowledge) can be acquired by immersion in the group that possesses and produces the expert knowledge. The modified imitation game can be a useful research tool for measuring interactional expertise within a community of practice and evaluating practitioners’ understanding of true experts.

Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email: [email protected]

Creating High-Resolution Microscopic Cross-Section Images of Hardwood Species Using Generative Adversarial Networks

Microscopic wood identification plays a critical role in many economically important areas in wood science. Historically, producing and curating relevant and representative microscopic cross-section images of wood species is limited to highly experienced and trained anatomists. This manuscript demonstrates the feasibility of generating synthetic microscopic cross-sections of hardwood species. We leveraged a publicly available dataset of 119 hardwood species to train a style-based generative adversarial network (GAN). The proposed GAN generated anatomically accurate cross-section images with remarkable fidelity to actual data. Quantitative metrics corroborated the capacity of the generative model in capturing complex wood structure by resulting in a Fréchet inception distance score of 17.38. Image diversity was calculated using the Structural Similarity Index Measure (SSIM). The SSIM results confirmed that the GAN approach can successfully synthesize diverse images. To confirm the usefulness and realism of the GAN generated images, eight professional wood anatomists in two experience levels participated in a visual Turing test and correctly identified fake and actual images at rates of 48.3 and 43.7%, respectively, with no statistical difference when compared to random guess. The generative model can synthesize realistic, diverse, and meaningful high-resolution microscope cross-section images that are virtually indistinguishable from real images. Furthermore, the framework presented may be suitable for improving current deep learning models, helping understand potential breeding between species, and may be used as an educational tool.

Research trends on CAPTCHA: A systematic literature

The advent of technology has crept into virtually all sectors and this has culminated in automated processes making use of the Internet in executing various tasks and actions. Web services have now become the trend when it comes to providing solutions to mundane tasks. However, this development comes with the bottleneck of authenticity and intent of users. Providers of these Web services, whether as a platform, as a software or as an Infrastructure use various human interaction proof’s (HIPs) to validate authenticity and intent of its users. Completely automated public turing test to tell computer and human apart (CAPTCHA), a form of IDS in web services is advantageous. Research into CAPTCHA can be grouped into two -CAPTCHA development and CAPTCH recognition. Selective learning and convolutionary neural networks (CNN) as well as deep convolutionary neural network (DCNN) have become emerging trends in both the development and recognition of CAPTCHAs. This paper reviews critically over fifty article publications that shows the current trends in the area of the CAPTCHA scheme, its development and recognition mechanisms and the way forward in helping to ensure a robust and yet secure CAPTCHA development in guiding future research endeavor in the subject domain.