In order to solve the problem that the trigger of poisoning samples would disappear or be destroyed by image preprocessing, resulting in the failure of attack performance, a covert poisoning attack method based on anti-interpolation operation is proposed. Through anti-interpolation calculation of the scaled target image, a poisoning image can be optimized. The poisoned image can be transformed into a target image with a specific trigger after scaling, and input into the model for training. The backdoor is inserted into the model to realize the poisoning attack on the model. Experimental results on the MNIST and CIFAR10 datasets show that compared with the existing methods, the proposed method can not only keep the success rate of poisoning attack basically unchanged, but also make the poisoning process more hidden.