Novel Preprocessors for Convolution Neural Networks

Fooling neural networks is a main concern in the process of Artificial Intelligence optimization. Character perturbation make part of a text unnoticeable for some systems, even for human observers. This research focuses the probe on a novel input preprocessing technique, which applies with the Convolutional Neural Networks for character recognition applications. Using Hand-written, Font, and Arabic Alphabet datasets, we show the CNN competitive results in classification. Then, we apply random deformation by mean of reshaping and relocation on the three testing datasets. As a reaction to change, the CNN demonstrates a confused behavior. Therefore, we design a Weightless Neural Network preprocessor that optimizes the shapes of deformed characters. Finally, we compare the impact of corrected input in improving the CNN performance. To measure the success of our preprocessor, we have applied it on well-known CNNs to compare the results between defected and rectified test datasets. Using Keras, we reconstructed LeNet-5, AlexNet and ZFNet for testing. Then, we conduct the test on the three databases to show how the recognition capability of CNNs would decline, then improve after preprocessing.