Realistic Scatterer Based Adversarial Attacks on SAR Image Classifiers
Tian Ye Rajgopal Kannan Viktor Prasanna Carl Busart Lance Kaplan
Adversarial attacks have highlighted the vulnerability of classifiers based
on machine learning for Synthetic Aperture Radar (SAR) Automatic Target
Recognition (ATR) tasks. An adversarial attack perturbs SAR images of on-ground
targets such that the classifiers are misled into making incorrect predictions.
However, many existing attacking techniques rely on arbitrary manipulation of
SAR images while overlooking the feasibility of executing the attacks on
real-world SAR imagery. Instead, adversarial attacks should be able to be
implemented by physical actions, for example, placing additional false objects
as scatterers around the on-ground target to perturb the SAR image and fool the
SAR ATR.
  In this paper, we propose the On-Target Scatterer Attack (OTSA), a
scatterer-based physical adversarial attack. To ensure the feasibility of its
physical execution, we enforce a constraint on the positioning of the
scatterers. Specifically, we restrict the scatterers to be placed only on the
target instead of in the shadow regions or the background. To achieve this, we
introduce a positioning score based on Gaussian kernels and formulate an
optimization problem for our OTSA attack. Using a gradient ascent method to
solve the optimization problem, the OTSA can generate a vector of parameters
describing the positions, shapes, sizes and amplitudes of the scatterers to
guide the physical execution of the attack that will mislead SAR image
classifiers. The experimental results show that our attack obtains
significantly higher success rates under the positioning constraint compared
with the existing method.