  Adversarial attacks meticulously generate minuscule, imperceptible
perturbations to images to deceive neural networks. Counteracting these,
adversarial purification methods seek to transform adversarial input samples
into clean output images to defend against adversarial attacks. Nonetheless,
extent generative models fail to effectively eliminate adversarial
perturbations, yielding less-than-ideal purification results. We emphasize the
potential threat of residual adversarial perturbations to target models,
quantitatively establishing a relationship between perturbation scale and
attack capability. Notably, the residual perturbations on the purified image
primarily stem from the same-position patch and similar patches of the
adversarial sample. We propose a novel adversarial purification approach named
Information Mask Purification (IMPure), aims to extensively eliminate
adversarial perturbations. To obtain an adversarial sample, we first mask part
of the patches information, then reconstruct the patches to resist adversarial
perturbations from the patches. We reconstruct all patches in parallel to
obtain a cohesive image. Then, in order to protect the purified samples against
potential similar regional perturbations, we simulate this risk by randomly
mixing the purified samples with the input samples before inputting them into
the feature extraction network. Finally, we establish a combined constraint of
pixel loss and perceptual loss to augment the model's reconstruction
adaptability. Extensive experiments on the ImageNet dataset with three
classifier models demonstrate that our approach achieves state-of-the-art
results against nine adversarial attack methods. Implementation code and
pre-trained weights can be accessed at
\textcolor{blue}{https://github.com/NoWindButRain/IMPure}.
