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The vulnerability of deep networks towards adversarial perturbations has motivated the researchers to design detection and mitigation algorithms. Inspired by the dropout and dropconnect algorithms as well as augmentation techniques, this paper presents “PixelMask” based data augmentation as an efficient method of reducing the sensitivity of convolutional neural networks (CNNs) towards adversarial attacks. In the proposed approach, samples generated using PixelMask are used as augmented data, which helps in learning robust CNN models. Experiments performed with multiple databases and architectures show that the proposed PixelMask based data augmentation approach improves the classification performance on adversarially perturbed images. The proposed defense mechanism can be applied effectively for different adversarial attacks and can easily be combined with any deep neural network (DNN) architecture to increase the robustness. The effectiveness of the proposed defense is demonstrated in gray-box, white-box, and unseen train-test attack scenarios. For example, on the CIFAR-10 database under adaptive attack (i.e., projected gradient descent), the proposed PixelMask is able to improve the recognition performance of CNN by at-least 22.69%. Another advantage of the proposed algorithm over several existing defense algorithms is that the proposed defense either is able to retain or increase the classification accuracy of clean examples.

Nowadays, many research tasks are concentrating on Social Media for Analyzing Sentiments and Opinions, Political Issues, Marketing Strategies and many more. Several text mining structures have been designed for different applications. Harassing is a category of claiming social turmoil in different structures and conduct toward a singular or group, to damage others. Investigation outcomes demonstrated that 7 young people out of 10 become the casualty of cyber bullying. Throughout the world, many prominent cases are existing due to the bad communications over the Web. So there could be suitable solutions for this problem and there is a need to eradicate the lacking in existing strategies in dealing problems with cyber bullying incidents. A prominent aim is to design a scheme to alert the people those who are using social networks and also to prevent them from bullying environments. Tweet corpus carries the messages in the text as well as it has ID, time, and so forth. The messages are imparted in informal form and furthermore, there is variety in the dialect. So, there is a requirement to operate a progression of filtration to handle the raw tweets before feature extraction and frequency extraction. The idea is to regard each tweet as a limited blend over a basic arrangement of topics, each of which is described by dissemination over words, and after that analyze tweets through such topic dispersions. Naturally, bullying topics might be related to higher probabilities for bullying words. An arrangement of training tweets with both bullying and non-bullying texts are required to take in a model that can derive topic distributions from tweets. Topic modeling is used to get lexical collocation designs in the irreverent content and create significant topics for a model.