## 7 Conclusions and Future Work  
A new image classifier model is proposed. The proposed model has a multi-section structure with a hierarchy of optimized class prototypes and hyperspherical class boundaries, which provides adaptive computation, perturbation absorption, and graceful degradation. A new training method, which combines self-knowledge distillation, the maximization of compactness of class distribution and interclass gap, the discretization of feature representations, and consistency regularization, is proposed. The applied loss function assembled from its constituent parts in a particular way depending on the result of perturbation detection and the presence of a new labeled and unlabeled data is presented. Consistency regularization makes it possible to utilize both labeled and unlabeled data to obtain a robust model and to implement continuous adaptation. Experimental results obtained from the Cifar10 and Cifar100 datasets show that the proposed approach provides high robustness to adversarial attacks with a maximum amplitude of perturbation equal to 1 and robustness to the presence of a single random bit-flip in 10% of the randomly selected tensors of the deep model. An increase in the level of adversarial perturbation mainly leads to an increase in the number of reject decisions, rather than false positives which represent a form of graceful degradation. A comparative analysis between the different class hierarchy levels shows that the upper level of class hierarchy is characterized by a higher level of robustness. It is a useful property for the implementation of graceful degradation mechanism under the influence of fault injection and adversarial attacks. Replacing the classification head based on prototypes with a dense layer as well as replacing ResNet blocks with Swin transformer blocks preserved the system property of resilience, but in many aspects led to a reduced absorption of perturbations and a reduced speed of adaptation. Although Swin transformer blocks provided slightly better robustness in perturbing effects, the speed of adaptation of the obtained classifier is noticeably slower. In all experiments, the accuracy of the classifier and the speed of performance recovery are noticeably higher if the intermediate outputs of the model are used. Experimental results also demonstrate that the proposed algorithm takes more than 32 times less steps (mini-batches) to recover the performance after adding the new class or the real concept drift between two or three classes than it does to retrain from scratch. At the same time, taking into account the outputs of intermediate sections allow the adaptation to be sped up by more than 20%. It has been experimentally proven that the proposed approach allows resources (time) to be saved when processing simple samples under normal conditions and increases computational resource allocation to improve the reliability of decisions when exposed to perturbations. The practical significance of the research derives from a new methodological basis for the development of resilient algorithms for classification image analysis under conditions of adversarial attacks, faults, and concept drifts. Future research should focus on the development of criteria, models, and methods for measuring and certifying the resilience of image classifiers. Special attention should also be paid to the question of providing a tradeoff between the classifier performance without perturbations and the level of model resilience under perturbation. This can be useful for the development of meta-learning algorithms aimed to provide an affordable resilience of image classifiers. **Author Contributions:** Supervision and conceptualization: V.K. ; methodology: V.K. and V.M. ; model and algorithm design: V.M. ; software, visualization, and validation: S.P. and A.M.; writing--original draft: V.M. and V.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable.