Applying Visual Cryptography to Enhance Text Captchas - IT Assignment Help

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Abstract: Nowadays, lots of applications and websites utilize text-based captchas to partially protect the authentication mechanism. However, in recent years, different ways have been exploited to automatically recognize text-based captchas especially deep learning-based ways, such as, convolutional neural network (CNN). Thus, we have to enhance the text captchas design. In this paper, using the features of the randomness for each encoding process in visual cryptography (VC) and the visual recognizability with naked human eyes, VC is applied to design and enhance text-based captcha. Experimental results using two typical deep learning-based attack models indicate the effectiveness of the designed method. By using our designed VC-enhanced text-based captcha (VCETC), the recognition rate is in some degree decreased.

1. Introduction - Nowadays, lots of applications and websites, including Baidu, Sina, Jingdong and many others, utilize text-based captchas to partially protect the authentication mechanism from certain types of attacks [1]. Text-based captchas belong to visual captchas. This type of captcha asks the user to identify some characters in an image deliberately rendered with some distortion and/or noise. Text-based captchas is widely used of since it is easily understood by most humans worldwide and since childhood we have been trained to recognize characters; it has a large brute force search space; its generation is easily automated without manual effort [2]. However, optical character recognition (OCR)-related techniques bring great challenges to text-based captchas. Text-based captcha enhanced methods and text-based captchas that automatically recognize ways—a.k.a breaking captchas [3–6]—are developed against each other. However, by machine learning attacks, 2D text-based captchas are easily broken. More importantly, deep learning-based breaking ways [4–6] have posed great challenges for text-based captchas, such as convolutional neural networks (CNN) [6–8]. One typical breaking way [6] has a series of blocks including a convolution layer, pooling layer and a full connection layer connected to the output layer with an activation function.

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