mlsanityData to 7 Oct 2026

A Survey of Adversarial CAPTCHAs on its History, Classification and Generation

arXiv 2311.13233 ↗First posted 22 Nov 2023cs.CR cs.AIcited by 0

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Abstract (arXiv)

Completely Automated Public Turing test to tell Computers and Humans Apart, short for CAPTCHA, is an essential and relatively easy way to defend against malicious attacks implemented by bots. The security and usability trade-off limits the use of massive geometric transformations to interfere deep model recognition and deep models even outperformed humans in complex CAPTCHAs. The discovery of adversarial examples provides an ideal solution to the security and usability trade-off by integrating adversarial examples and CAPTCHAs to generate adversarial CAPTCHAs that can fool the deep models. In this paper, we extend the definition of adversarial CAPTCHAs and propose a classification method for adversarial CAPTCHAs. Then we systematically review some commonly used methods to generate adversarial examples and methods that are successfully used to generate adversarial CAPTCHAs. Also, we analyze some defense methods that can be used to defend adversarial CAPTCHAs, indicating potential threats to adversarial CAPTCHAs. Finally, we discuss some possible future research directions for adversarial CAPTCHAs at the end of this paper.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 102 references that are in the map. First posted on arXiv 22 Nov 2023.

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References · 32

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
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  6. 2016Adversarial examples in the physical worldcited by 1,762
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  10. 2016Universal adversarial perturbationscited by 774
  11. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
  12. 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 591
20 more
  1. 2017One pixel attack for fooling deep neural networkscited by 579
  2. 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
  3. 2018Generating Adversarial Examples with Adversarial Networkscited by 256
  4. 2019AdvHat: Real-world adversarial attack on ArcFace Face ID systemcited by 161
  5. 2019Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognitioncited by 123
  6. 2021Adversarial Sticker: A Stealthy Attack Method in the Physical Worldcited by 92
  7. 2019Adversarial camera stickers: A physical camera-based attack on deep learning systemscited by 69
  8. 2017DolphinAtack: Inaudible Voice Commandscited by 65
  9. 2017Adversarial Transformation Networks: Learning to Generate Adversarial Examplescited by 60
  10. 2017Houdini: Fooling Deep Structured Prediction Modelscited by 50
  11. 2022Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Inputcited by 49
  12. 2018Did you hear that? Adversarial Examples Against Automatic Speech Recognitioncited by 48
  13. 2019Harnessing the Vulnerability of Latent Layers in Adversarially Trained Modelscited by 26
  14. 2019AdvGAN++ : Harnessing latent layers for adversary generationcited by 20
  15. 2019Adversarial CAPTCHAscited by 16
  16. 2020Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attackscited by 8
  17. 2020BeCAPTCHA: Detecting Human Behavior in Smartphone Interaction using Multiple Inbuilt Sensorscited by 7
  18. 2021Direction-Aggregated Attack for Transferable Adversarial Examplescited by 6
  19. 2022aaeCAPTCHA: The Design and Implementation of Audio Adversarial CAPTCHAcited by 5
  20. 2016Usability Investigation on the Localization of Text CAPTCHAs: Take Chinese Characters as a Case Studycited by 2

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