Adversarial Machine Learning in Text Analysis and Generation
arXiv:2101.08675
Abstract
The research field of adversarial machine learning witnessed a significant interest in the last few years. A machine learner or model is secure if it can deliver main objectives with acceptable accuracy, efficiency, etc. while at the same time, it can resist different types and/or attempts of adversarial attacks. This paper focuses on studying aspects and research trends in adversarial machine learning specifically in text analysis and generation. The paper summarizes main research trends in the field such as GAN algorithms, models, types of attacks, and defense against those attacks.
References in corpus (20)
- Sequence to Sequence Learning with Neural Networks
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Microsoft COCO Captions: Data Collection and Evaluation Server
- Certified Adversarial Robustness via Randomized Smoothing
- Understanding Neural Networks through Representation Erasure
- Professor Forcing: A New Algorithm for Training Recurrent Networks
- Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs
- An Actor-Critic Algorithm for Sequence Prediction
- GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution
- Deceiving Google's Perspective API Built for Detecting Toxic Comments
- Adversarial Generation of Natural Language
- Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGAN
- ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
- Adversarial Texts with Gradient Methods
- Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble
- Improving Sequence-to-Sequence Learning via Optimal Transport
- DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation
- Text Generation with Exemplar-based Adaptive Decoding
- Understanding and Diagnosing Vulnerability under Adversarial Attacks
- GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis