Adversarial attacks and defenses in explainable artificial intelligence: A survey

Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions. However, recent advances in adversarial machine learning (AdvML) highlight the limitations and vulnerabilities of state-of-the-art explanation methods, putting their security and trustworthiness into question. The possibility of manipulating, fooling or fairwashing evidence of the model's reasoning has detrimental consequences when applied in high-stakes decision-making and knowledge discovery. This survey provides a comprehensive overview of research …

adversarial adversarial attacks art artificial artificial intelligence attacks cs.ai cs.cr cs.cv cs.lg debugging deep learning defenses intelligence limitations machine machine learning predictions question remedy security state survey trustworthiness vulnerabilities xai