Dezdemona Gjylapi

Wrapper-Based Adversarial Input Screening for Deep Image Classifiers Using Feature Squeezing and Logit-Space Inconsistency

A wrapper-based adversarial input screening approach that compares a classifier’s output on an original image with its output after benign feature-squeezing transformations is evaluated, supporting the use of median filtering with logit-space ℓ2 inconsistency as a tool for screening adversarial inputs to image classifiers, but its effectiveness depends on dataset complexity, classifier behaviour, and attack adaptivity.