Reverse Turing test

About: Reverse Turing test is a research topic. Over the lifetime, 119 publications have been published within this topic receiving 3907 citations.

CAPTCHA: using hard AI problems for security

Abstract: We introduce captcha, an automated test that humans can pass, but current computer programs can't pass: any program that has high success over a captcha can be used to solve an unsolved Artificial Intelligence (AI) problem. We provide several novel constructions of captchas. Since captchas have many applications in practical security, our approach introduces a new class of hard problems that can be exploited for security purposes. Much like research in cryptography has had a positive impact on algorithms for factoring and discrete log, we hope that the use of hard AI problems for security purposes allows us to advance the field of Artificial Intelligence. We introduce two families of AI problems that can be used to construct captchas and we show that solutions to such problems can be used for steganographic communication. captchas based on these AI problem families, then, imply a win-win situation: either the problems remain unsolved and there is a way to differentiate humans from computers, or the problems are solved and there is a way to communicate covertly on some channels.

Secure authentication systems and methods

Abstract: Systems and methods are provided for authentication by combining a Reverse Turing Test (RTT) with password-based user authentication protocols to provide improved resistance to brute force attacks. In accordance with one embodiment of the invention, a method is provided for user authentication, the method including receiving a username/password pair associated with a user; requesting one or more responses to a first Reverse Turing Test (RTT); and granting access to the user if a valid response to the first RTT is received and the username/password pair is valid.

Pessimal print: a reverse Turing test

Abstract: We exploit the gap in ability between human and machine vision systems to craft a family of automatic challenges that tell human and machine users apart via graphical interfaces including Internet browsers. Turing proposed (1950) a method whereby human judges might validate "artificial intelligence" by failing to distinguish between human and machine interlocutors. Stimulated by the "chat room problem", and influenced by the CAPTCHA project of Blum et al. (2000), we propose a variant of the Turing test using pessimal print: that is, low-quality images of machine-printed text synthesized pseudo-randomly over certain ranges of words, typefaces, and image degradations. We show experimentally that judicious choice of these ranges can ensure that the images are legible to human readers but illegible to several of the best present-day optical character recognition (OCR) machines. Our approach is motivated by a decade of research on performance evaluation of OCR machines and on quantitative stochastic models of document image quality. The slow pace of evolution of OCR and other species of machine vision over many decades suggests that pessimal print will defy automated attack for many years. Applications include 'bot' barriers and database rationing.

Method and apparatus for filtering e-mail infected with a previously unidentified computer virus

Abstract: E-mail which may be infected by a computer virus is advantageously filtered by incorporating a “Reverse Turing Test” to verify that the source of a potentially infected e-mail is human and not a machine, and that the message was intentionally transmitted by the apparent sender. Such a test may, for example, involve asking a question which will be easy for a human to answer correctly but quite difficult for a machine to do so. The e-mail may be deemed to be potentially infected based on an analysis of executable code which is attached to the e-mail, or merely based on the fact that executable code is attached. The e-mail may also be deemed to be potentially infected based on additional factors, such as, for example, the identity of the sender and past experiences therewith. Spam E-mail may also be advantageously filtered together with virus-containing e-mail with use of a single common filtering system.

ARTiFACIAL: automated reverse turing test using FACIAL features

Abstract: Web services designed for human users are being abused by computer programs (bots). The bots steal thousands of free email accounts in a minute; participate in online polls to skew results; and irritate people by joining online chat rooms. These real-world issues have recently generated a new research area called Human Interactive Proofs (HIP), whose goal is to defend services from malicious attacks by differentiating bots from human users. In this paper, we propose a new HIP algorithm based on detecting human face and facial features. Human faces are the most familiar object to humans, rendering it possibly the best candidate for HIP. We conducted user studies and showed the ease of use of our system to human users. We designed attacks using the best existing face detectors and demonstrated the difficulty to bots.