Biblio

Art is the perfect way for people to express their emotions in a way that words are unable to do. By simply looking at art, we can understand a person’s creativity and thoughts. In former times, artists spent a great deal of time creating an image of varied styles. In the current deep learning era, we are able to create images of different styles as we prefer within a short period of time. Neural style transfer is the most popular and widely used deep learning application that applies the desired style to the content image, which in turn generates an output image that is a combination of both style and the content of the original image. In this paper we have implemented the neural style transfer model with two architectures namely Vgg19 and Alexnet. This paper compares the output-styled image and the total loss obtained through VGG19 and Alexnet architectures. In addition, three different activation functions are used to compare quality and total loss of output styled images within Alexnet architectures.

The CAPTCHA is an effective method commonly used in live interactive proofs on the Internet. The widely used CAPTCHAs are text-based schemes. In this paper, we document how we have broken such text-based scheme used by a website CAPTCHA. We use the sliding window to segment 1001 pieces of CAPTCHA to get 5900 images with single-character useful information, a total of 25 categories. In order to make the convolution neural network learn more image features, we augmented the data set to get 129924 pictures. The data set is trained and tested in AlexNet and GoogLeNet to get the accuracy of 87.45% and 98.92%, respectively. The experiment shows that the optimized network parameters can make the accuracy rate up to 92.7% in AlexNet and 98.96% in GoogLeNet.