Adversarial Black-Box Attacks On Text Classifiers Using Multi-Objective
  Genetic Optimization Guided By Deep Networks
  We propose a novel genetic-algorithm technique that generates black-box
adversarial examples which successfully fool neural network based text
classifiers. We perform a genetic search with multi-objective optimization
guided by deep learning based inferences and Seq2Seq mutation to generate
semantically similar but imperceptible adversaries. We compare our approach
with DeepWordBug (DWB) on SST and IMDB sentiment datasets by attacking three
trained models viz. char-LSTM, word-LSTM and elmo-LSTM. On an average, we
achieve an attack success rate of 65.67% for SST and 36.45% for IMDB across the
three models showing an improvement of 49.48% and 101% respectively.
Furthermore, our qualitative study indicates that 94% of the time, the users
were not able to distinguish between an original and adversarial sample.
