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Neural networks are frequently used for text classification, but can be vulnerable to misclassification caused by adversarial examples: input produced by introducing small perturbations that cause the neural network to output an incorrect classification. Previous attempts to generate black-box adversarial texts have included variations of generating nonword misspellings, natural noise, synthetic noise, along with lexical substitutions. This paper proposes a defense against black-box adversarialdoi:10.5120/ijca2019919384 fatcat:qc2qpljyu5a5hcv4mmdt7nvnzy