Remotely Telling Humans and Computers Apart: An Unsolved Problem

The ability to tell humans and computers apart is imperative to protect many services from misuse and abuse. For this purpose, tests called CAPTCHAs or HIPs have been designed and put into production. Recent history shows that most (if not all) can be broken given enough time and commercial interest: CAPTCHA design seems to be a much more difficult problem than previously thought. The assumption that difficult-AI problems can be easily converted into valid CAPTCHAs is misleading. There are also some extrinsic problems that do not help, especially the big number of inhouse designs that are put into production without any prior public critique. In this paper we present a state-of-the-art survey of current HIPs, including proposals that are now into production. We classify them regarding their basic design ideas. We discuss current attacks as well as future attack paths, and we also present common errors in design, and how many implementation flaws can transform a not necessarily bad idea into a weak CAPTCHA. We present examples of these flaws, using specific well-known CAPTCHAs. In a more theoretical way, we discuss the threat model: confronted risks and countermeasures. Finally, we introduce and discuss some desirable properties that new HIPs should have, concluding with some proposals for future work, including methodologies for design, implementation and security assessment.