SAR-DiSCO: Differentiable Scattering Coefficient Optimization for 3D Adversarial Attacks on SAR Target Recognition

Synthetic aperture radar (SAR) automatic target recognition (ATR) systems are highly vulnerable to adversarial attacks, which can cause classification errors and threaten system reliability. Many existing attacks on SAR ATR inject perturbations directly into the 2D image domain without explicitly modeling target-surface scattering or the SAR image formation process. Such perturbations do not necessarily correspond to consistent target-level modifications across 3D viewing conditions. To address this gap, we propose SAR-DiSCO, a simulation-based adversarial attack framework that optimizes target-surface scattering coefficients through differentiable SAR rendering. The proposed approach employs a custom differentiable SAR renderer to perform end-to-end gradient optimization of vertex-wise scattering coefficients, producing adversarial perturbations while preserving target geometry. Specifically, we (1) build an end-to-end differentiable SAR imaging and recognition framework that links 3D surface coefficients to classification predictions, and (2) design a loss function that balances misclassification and scattering-coefficient deviation. We then (3) iteratively update the coefficients via backpropagation for view-specific attacks or shared multi-view optimization, without requiring a separately fitted surrogate mapping. Experiments on a simulated five-class vehicle dataset demonstrate an average view-specific fooling rate of 88.96% across five CNN- and Transformer-based classifiers. Further experiments demonstrate strong cross-architecture transferability and partial cross-view effectiveness when a single coefficient field is shared across viewing conditions.