AdvCodeMix: Adversarial Attack on Code-Mixed Data
Research on adversarial attacks are becoming widely popular in the recent years. One of the unexplored areas where prior research is lacking is the effect of adversarial attacks on code-mixed data. Therefore, in the present work, we have explained the first generalized framework on text perturbation to attack code-mixed classification models in a black-box setting. We rely on various perturbation techniques that preserve the semantic structures of the sentences and also obscure the attacks from the perception of a human user. The present methodology leverages the importance of a token to decide where to attack by employing various perturbation strategies. We test our strategies on various sentiment classification models trained on Bengali-English and Hindi-English code-mixed datasets, and reduce their F1-scores by nearly 51 % and 53 % respectively, which can be further reduced if a larger number of tokens are perturbed in a given sentence.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackSentenceSentiment AnalysisSentiment ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
BeamAttack: Generating High-quality Textual Adversarial Examples through Beam Search and Mixed Semantic Spaces
Natural language processing models based on neural networks are vulnerable to adversarial examples. These adversarial examples are imperceptible to human readers but can mislead models to make the wrong predictions. In a…
Heuristic SearchTowards Generating Adversarial Examples on Mixed-type Data
The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues. For many safety-critical ML tasks, such as financial forecasting, fraudulent detec…
Anomaly DetectionVocal Bursts Type PredictionElevating Code-mixed Text Handling through Auditory Information of Words
With the growing popularity of code-mixed data, there is an increasing need for better handling of this type of data, which poses a number of challenges, such as dealing with spelling variations, multiple languages, diff…
Language ModelingLanguage ModellingAvailability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting
The forecast of electrical loads is essential for the planning and operation of the power system. Recently, advances in deep learning have enabled more accurate forecasts. However, deep neural networks are prone to adver…
Adversarial AttackLoad ForecastingPositionGame Theoretic Mixed Experts for Combinational Adversarial Machine Learning
Recent advances in adversarial machine learning have shown that defenses considered to be robust are actually susceptible to adversarial attacks which are specifically customized to target their weaknesses. These defense…
Adversarial Defense