# An Object Detection based Solver for Google's Image reCAPTCHA v2

arXiv: https://arxiv.org/abs/2104.03366 · HTML: https://mlsanity.com/paper/2104.03366/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 56 references that are in the map.
- First posted on arXiv 7 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Focal Loss for Dense Object Detection](https://mlsanity.com/paper/1708.02002/) (2017)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)

## Built on by (most cited first, 8 of 8)

- [Infrastructure for AI Agents](https://mlsanity.com/paper/2501.10114/) (2025)
- [Breaking reCAPTCHAv2](https://mlsanity.com/paper/2409.08831/) (2024)
- [Aura-CAPTCHA: A Reinforcement Learning and GAN-Enhanced Multi-Modal CAPTCHA System](https://mlsanity.com/paper/2508.14976/) (2025)
- [Next-Gen CAPTCHAs: Leveraging the Cognitive Gap for Scalable and Diverse GUI-Agent Defense](https://mlsanity.com/paper/2602.09012/) (2026)
- [DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning](https://mlsanity.com/paper/2109.02874/) (2021)
- [A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications](https://mlsanity.com/paper/2409.15802/) (2024)
- [VIPER Strike: Defeating Visual Reasoning CAPTCHAs via Structured Vision-Language Inference](https://mlsanity.com/paper/2601.06461/) (2026)
- [CAPTCHAs in the Agentic Era: Solvers That Learn from Every Encounter](https://mlsanity.com/paper/2609.02393/) (2026)

## Abstract (arXiv)

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 experimental 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.