receiving an input data from a sensor, wherein the input data is indicative of image, radar, sonar, or sound information;

generating a perturbed input data set utilizing the input data, wherein the perturbed input data set includes perturbed data;

denoising, via a pre-trained denoiser, the perturbed input data set to generate a denoised data set;

training the machine-learning network utilizing the denoised data set, wherein the machine-learning network is configured to reject the denoised data set, utilizing a rejector, when a classification probability falls below a classification threshold, wherein the classification threshold is associated with classification the denoised data set, wherein the rejector is trained to discriminate between correctly classified inputs and misclassified denoised inputs; and

in response to the classification probability falling below the classification threshold, outputting an abstain classification associated with the input data, wherein the abstain classification is ignored for classifying.