Biblio

With the development of the Internet, the captcha technology has also been widely used. Captcha technology is used to distinguish between humans and machines, namely Completely Automated Public Turing test to tell Computers and Humans Apart. In this paper, an end-to-end deep CNN-RNN network model is constructed by studying the captcha recognition technology, which realizes the recognition of 4-character text captcha. The CNN-RNN model first constructs a deep residual convolutional neural network based on the residual network structure to accurately extract the input captcha picture features. Then, through the constructed variant RNN network, that is, the two-layer GRU network, the deep internal features of the captcha are extracted, and finally, the output sequence is the 4-character captcha. The experiments results show that the end-to-end deep CNN-RNN network model has a good performance on different captcha datasets, achieving 99% accuracy. And experiment on the few samples dataset which only has 4000 training samples also shows an accuracy of 72.9 % and a certain generalization ability.

A “Completely Automated Public Turing Test to Tell Computers and Humans Apart” (CAPTCHA) widely used online services so that prevents bots from automatic getting a large of accounts. Interactive video type CAPTCHAs that attempt to detect this attack by using delay time due to communication relays have been proposed. However, these approaches remain insufficiently resistant to bots. We propose a CAPTCHA that combines resistant to automated and relay attacks. In our CAPTCHA, the users recognize a moving object (target object) from among a number of randomly appearing decoy objects and tracks the target with mouse cursor. The users pass the test when they were able to track the target for a certain time. Since the target object moves quickly, the delay makes it difficult for a remote solver to break the CAPTCHA during a relay attack. It is also difficult for a bot to track the target using image processing because it has same looks of the decoys. We evaluated our CAPTCHA's resistance to relay and automated attacks. Our results show that, if our CAPTHCA's parameters are set suitable value, a relay attack cannot be established economically and false acceptance rate with bot could be reduced to 0.01% without affecting human success rate.