Papers with black-box population-based optimization algorithm
Generating Natural Language Adversarial Examples (D18-1)
Copied to clipboard
| Challenge: | Recent research has shown that deep neural networks are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. |
| Approach: | They propose to generate adversarial examples that fool well-trained sentiment analysis and textual entailment models by using a black-box population-based optimization algorithm. |
| Outcome: | The proposed model is able to fool well-trained sentiment analysis and textual entailment models with success rates of 97% and 70%, respectively. |