Canvas Method

2000년 도입 · 논문 1편에서 사용

Canvas Method is a method for inference attacks on object detection models. It draws a predicted bounding box distribution on an empty canvas for an attack model input. The canvas is initially set to an image of 300$\times$300 pixels in size, where every pixel has a value of zero and the boxes drawn on the canvas have the same center as the predicted boxes and the same intensity as the prediction scores.

출처: Membership Inference Attacks Against Object Detection Models

소개 논문: Membership Inference Attacks Against Object Detection Models