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Recently, a research team from ETH Zurich has made a shocking breakthrough by successfully cracking Google's reCAPTCHA v2 system with a success rate of 100%! This research has sparked widespread discussions about the future of image verification. The team utilized an advanced image recognition algorithm called YOLO, which, by segmenting and classifying images, enables their system to automatically solve all three types of challenges in reCAPTCHA v2.

Researchers have proposed the 'Human Identity Credentials' (PHC) system as a new alternative to traditional CAPTCHA verification, aimed at confirming online users as real humans. This system provides a unique identity credential for each user through zero-knowledge proof encryption technology, which is stored on personal devices to protect privacy, serving as a replacement for CAPTCHA and biometric verification. However, the system faces issues such as credential abuse, concentration of power, and barriers to use. The research suggests that governments should test the feasibility of PHC through pilot projects, while calling on tech companies to take responsibility for the problems arising from AI, such as misleading AI outputs.

Recently, a new captcha bypass tool called GPT4o Captcha Bypass has caught the industry's attention. This tool is a command-line interface (CLI) application that utilizes Python and Selenium technology to test and crack various types of captcha systems.The main features of GPT4o Captcha Bypass include: 1. Supports multiple captcha types: This tool can handle puzzle captchas, text captchas, complex text captchas, as well as the widely used reCAPTCHA system. 2. Integrated advanced AI technology: T

The multi-modal large model 'Ferret' developed by Apple's AI/ML team in collaboration with Columbia University has successfully tackled Google's CAPTCHAs, being capable of recognizing traffic lights and improving the accuracy of large models in 'see-say-answer' tasks. Ferret's innovation lies in its ability to integrate spatial understanding of references and locations while comprehending semantics and targets, distinguishing it from traditional multi-modal models. By employing a hybrid regional representation method that combines discrete coordinates and continuous features, the model has demonstrated exceptional performance in multi-task evaluations, particularly in reference and visual grounding.

{1: Researchers found that robots outperform humans in both accuracy and speed when solving captchas. 2: Researchers discovered that current captchas can no longer effectively differentiate between robots and humans, necessitating a more behaviorally driven dynamic approach. 3: Captcha technology needs to adopt more dynamic methods to address security issues.}