TY - GEN
T1 - A sigma-lognormal model for character level CAPTCHA generation
AU - Ramaiah, Chetan
AU - Plamondon, Rejean
AU - Govindaraju, Venu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/20
Y1 - 2015/11/20
N2 - Word level handwritten CAPTCHA generation involves picking a handwritten word from a pre-existing database and cumulatively applying distortions and noise models. In principle, the addition of distortion and noise makes the CAPTCHA robust to automated attacks. However, the primary drawback of the word level CAPTCHA generation is that it limits us to words that already exist in our data set. If the primary building block of this approach was a character, we could move away from a lexicon based CAPTCHA generation and generate CAPTCHAs which are resistant to a dictionary based attack. In this paper, we propose a Sigma-Lognormal based approach to generate character level CAPTCHAs. Next, we increase the robustness of the model by applying ideas from accents in handwriting to our problem. Finally, we demonstrate the efficacy of our approach by simulating an attack by an automated word recognizer.
AB - Word level handwritten CAPTCHA generation involves picking a handwritten word from a pre-existing database and cumulatively applying distortions and noise models. In principle, the addition of distortion and noise makes the CAPTCHA robust to automated attacks. However, the primary drawback of the word level CAPTCHA generation is that it limits us to words that already exist in our data set. If the primary building block of this approach was a character, we could move away from a lexicon based CAPTCHA generation and generate CAPTCHAs which are resistant to a dictionary based attack. In this paper, we propose a Sigma-Lognormal based approach to generate character level CAPTCHAs. Next, we increase the robustness of the model by applying ideas from accents in handwriting to our problem. Finally, we demonstrate the efficacy of our approach by simulating an attack by an automated word recognizer.
UR - https://www.scopus.com/pages/publications/84962596141
U2 - 10.1109/ICDAR.2015.7333905
DO - 10.1109/ICDAR.2015.7333905
M3 - Conference contribution
AN - SCOPUS:84962596141
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 966
EP - 970
BT - 13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
PB - IEEE Computer Society
T2 - 13th International Conference on Document Analysis and Recognition, ICDAR 2015
Y2 - 23 August 2015 through 26 August 2015
ER -