Fast Algorithms for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Interactive Proofs and Document Image Analysis
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Pessimal Print: A Reverse Turing Test
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Designing human friendly human interaction proofs (HIPs)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Distortion estimation techniques in solving visual CAPTCHAs
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Shape context and chamfer matching in cluttered scenes
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Recognizing objects in adversarial clutter: breaking a visual captcha
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ScatterType: A Legible but Hard-to-Segment CAPTCHA
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Spam Filtering Based On The Analysis Of Text Information Embedded Into Images
The Journal of Machine Learning Research
Usability of CAPTCHAs or usability issues in CAPTCHA design
Proceedings of the 4th symposium on Usable privacy and security
A low-cost attack on a Microsoft captcha
Proceedings of the 15th ACM conference on Computer and communications security
Balancing usability and security in a video CAPTCHA
Proceedings of the 5th Symposium on Usable Privacy and Security
Synthetic handwritten CAPTCHAs
Pattern Recognition
The robustness of a new CAPTCHA
Proceedings of the Third European Workshop on System Security
Decaptcha: breaking 75% of eBay audio CAPTCHAs
WOOT'09 Proceedings of the 3rd USENIX conference on Offensive technologies
Attacks and design of image recognition CAPTCHAs
Proceedings of the 17th ACM conference on Computer and communications security
Effects of age groups and distortion types on text-based CAPTCHA tasks
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: users and applications - Volume Part IV
Text-based CAPTCHA strengths and weaknesses
Proceedings of the 18th ACM conference on Computer and communications security
AniCAP: an animated 3d CAPTCHA scheme based on motion parallax
CANS'11 Proceedings of the 10th international conference on Cryptology and Network Security
A novel architecture for the generation of picture based CAPTCHA
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Enhanced STE3D-CAP: a novel 3d CAPTCHA family
ISPEC'12 Proceedings of the 8th international conference on Information Security Practice and Experience
Security and usability challenges of moving-object CAPTCHAs: decoding codewords in motion
Security'12 Proceedings of the 21st USENIX conference on Security symposium
Segmentation of CAPTCHAs based on complex networks
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Proposal of CAPTCHA using three dimensional objects
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
A survey and analysis of current CAPTCHA approaches
Journal of Web Engineering
SPIDER: A platform for managing SIP-based Spam over Internet Telephony SPIT
Journal of Computer Security
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Human interaction proofs (HIPs) have become common place on the internet due to their effectiveness in deterring automated abuse of online services intended for humans. However, there is a co-evolutionary arms race in progress and these proofs are becoming more difficult for genuine users while attackers are getting better at breaking existing HIPs. We studied various popular HIPs on the internet to understand their strength and human friendliness. To determine HIP strength, we adopted a direct approach of building computer attacks using image processing and machine learning techniques. To understand human-friendliness, a sequence of users studies were conducted to investigate HIP character recognition by humans under a variety of visual distortions and clutter commonly employed in reading-based HIPs. We found that many of the online HIPs are pure recognition tasks that can be easily broken using machine learning. The stronger HIPs tend to pose a combination of segmentation and recognition challenges. Further, the HIP user studies show that given correct segmentation, computers are much better at HIP character recognition than humans. In light of these results, we propose that segmentation-based reading challenges are the future for building stronger human-friendly HIPs. An example of such a segmentation-based HIP is presented with a preliminary assessment of its strength and human-friendliness.