An Object Detection based Solver for Google’s Image reCAPTCHA v2


Md Imran Hossen, Yazhou Tu, Md Fazle Rabby, and Md Nazmul Islam, University of Louisiana at Lafayette; Hui Cao, Xi'an Jiaotong University; Xiali Hei, University of Louisiana at Lafayette


Previous work showed that reCAPTCHA v2's image challenges could be solved by automated programs armed with Deep Neural Network (DNN) image classifiers and vision APIs provided by off-the-shelf image recognition services. In response to emerging threats, Google has made significant updates to its image reCAPTCHA v2 challenges that can render the prior approaches ineffective to a great extent.

In this paper, we investigate the robustness of the latest version of reCAPTCHA v2 against advanced object detection based solvers. We propose a fully automated object detection based system that breaks the most advanced challenges of reCAPTCHA v2 with an online success rate of 83.25\%, the highest success rate to date, and it takes only 19.93 seconds (including network delays) on average to crack a challenge. We also study the updated security features of reCAPTCHA v2, such as anti-recognition mechanisms, improved anti-bot detection techniques, and adjustable security preferences. Our extensive experiments show that while these security features can provide some resistance against automated attacks, adversaries can still bypass most of them. Our experiment findings indicate that the recent advances in object detection technologies pose a severe threat to the security of image captcha designs relying on simple object detection as their underlying AI problem.

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@inproceedings {259735,
author = {Md Imran Hossen and Yazhou Tu and Md Fazle Rabby and Md Nazmul Islam and Hui Cao and Xiali Hei},
title = {An Object Detection based Solver for {Google{\textquoteright}s} Image {reCAPTCHA} v2},
booktitle = {23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020)},
year = {2020},
isbn = {978-1-939133-18-2},
address = {San Sebastian},
pages = {269--284},
url = {},
publisher = {USENIX Association},
month = oct