Towards Assessing the Synthetic-to-Measured Adversarial Vulnerability of
  SAR ATR
Bowen Peng Bo Peng Jingyuan Xia Tianpeng Liu Yongxiang Liu Li Liu
Recently, there has been increasing concern about the vulnerability of deep
neural network (DNN)-based synthetic aperture radar (SAR) automatic target
recognition (ATR) to adversarial attacks, where a DNN could be easily deceived
by clean input with imperceptible but aggressive perturbations. This paper
studies the synthetic-to-measured (S2M) transfer setting, where an attacker
generates adversarial perturbation based solely on synthetic data and transfers
it against victim models trained with measured data. Compared with the current
measured-to-measured (M2M) transfer setting, our approach does not need direct
access to the victim model or the measured SAR data. We also propose the
transferability estimation attack (TEA) to uncover the adversarial risks in
this more challenging and practical scenario. The TEA makes full use of the
limited similarity between the synthetic and measured data pairs for blind
estimation and optimization of S2M transferability, leading to feasible
surrogate model enhancement without mastering the victim model and data.
Comprehensive evaluations based on the publicly available synthetic and
measured paired labeled experiment (SAMPLE) dataset demonstrate that the TEA
outperforms state-of-the-art methods and can significantly enhance various
attack algorithms in computer vision and remote sensing applications. Codes and
data are available at https://github.com/scenarri/S2M-TEA.