The invention belongs to the field of image recognition, particularly relates to an adversarial sample generation method,
system and device for an
outlier removal method, and aims to solve the problems that an adversarial sample adopted by existing classification model training based on
deep learning cannot make an image classification error under an
outlier removal method; and therefore, the trained classification model is poor in robustness and low in accuracy. The method comprises the following steps: acquiring a training
data set with category labels, inputting three-dimensional
point cloud data into a classification model, calculating classification loss, respectively calculating the gradient of the classification loss relative to the three-dimensional
point cloud data and the gradient of the classification loss relative to
outlier-removed three-dimensional
point cloud data, and fusing the two gradients by multiplying a scaling factor to generate fusion disturbance, and applying the fusion disturbance to the three-dimensional point
cloud data for repeated iteration to generate an adversarial sample. The generated adversarial samples can still cause image classification errorsunder the condition that outliers are removed, and the robustness and classification accuracy of the trained model are improved.