Anyone who has been surfing the web for a while is probably used to clicking through a CAPTCHA grid of street images, identifying everyday objects to prove that they’re a human and not an automated bot. Now, though, new research claims that locally run bots using specially trained image-recognition models can match human-level performance in this style of CAPTCHA, achieving a 100 percent success rate despite being decidedly not human.

ETH Zurich PhD student Andreas Plesner and his colleagues’ new research, available as a pre-print paper, focuses on Google’s ReCAPTCHA v2, which challenges users to identify which street images in a grid contain items like bicycles, crosswalks, mountains, stairs, or traffic lights. Google began phasing that system out years ago in favor of an “invisible” reCAPTCHA v3 that analyzes user interactions rather than offering an explicit challenge.

Despite this, the older reCAPTCHA v2 is still used by millions of websites. And even sites that use the updated reCAPTCHA v3 will sometimes use reCAPTCHA v2 as a fallback when the updated system gives a user a low “human” confidence rating.

  • werefreeatlast@lemmy.world
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    3 months ago

    Recaptcha 4.0… what do you think about this image…an image of a kid riding their bike without any protective gear on a freeway.

    AI: a bike with a kid on it on a road. Perfectly fine.

      • T156@lemmy.world
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        3 months ago

        It’d be a bit unreliable, though. Not everyone has the same reaction to the same thing, nor do they express it in a similar way.

        Someone might think a snake or a spider is cute, whereas another would want to incinerate it on the spot. A third might be concerned because they seem to be injured, etc.

        Not to mention that image recognition/emotional analysis has been an ongoing field of research for some time. Making the link is not overly difficult.