Input-specific Attention Subnetworks for Adversarial Detection. (arXiv:2203.12298v1 [cs.CL])

Self-attention heads are characteristic of Transformer models and have been
well studied for interpretability and pruning. In this work, we demonstrate an
altogether different utility of attention heads, namely for adversarial
detection. Specifically, we propose a method to construct input-specific
attention subnetworks (IAS) from which we extract three features to
discriminate between authentic and adversarial inputs. The resultant detector
significantly improves (by over 7.5%) the state-of-the-art adversarial
detection accuracy for the BERT encoder on 10 NLU datasets with 11 different …

adversarial attention detection input