Conditional Random Field-Based Adversarial Attack Against SAR Target Detection

The existence of adversarial examples causes serious security risks when deep neural networks are applied to synthetic aperture radar (SAR) target detection. In SAR image processing, the added small disturbances can cause the model to output incorrect predictions. Due to the multipath effect in the propagation of detection signals, there are complex interactions between targets and their surroundings serving as supportive clues for target detection. The interactions are manifested as tight correlations between pixels and contextual information in the SAR image (where context refers to various relationships, e.g., target-to-target co-occurrence relationships). In this letter, we proposed a novel conditional random field-based adversarial attack (CRFA) method, which disturbs the intrinsic interactions between the target and its surroundings. To the best of our knowledge, we are the first to exploit the contextual information for attacking the SAR target detector. We formulate the attack as an optimization problem and design the context information loss to calculate the energy differences in local feature patterns before and after perturbation. By maximizing the energy differences, the context area information around the target is destroyed, and the detector outputs the candidate box with a slight shift, even ignoring the ground truth and missing targets. Extensive experimental results on the SAR Ship Detection dataset (SSDD) demonstrate that our proposed algorithm reduces mAP by 4.29% on existing object detection models, validating the effectiveness of the method.