Memory Defense: More Robust Classification via a Memory-Masking Autoencoder

  • We developed a robust autoencoder with one-hot memory masking to mitigate adversarial attacks.
  • The proximity approximation model can retrieve an image’s relevant memory features and reconstruct it with a repaired label.
  • The enhanced deep neural architecture significantly improved the robustness of DNN for an image classification task.