## 1 Introduction  
### Motivation  
Image classification is one of the most common tasks in the field of artificial intelligence. Classification analysis of visual objects is often a component of safety-critical applications, such as medical diagnostics and autopilots of public transport and combat drones. It is used in production processes, traffic flow monitoring, infrastructure inspection, industrial facilities, and other similar tasks. Therefore, there is a need to ensure the resilience of artificial intelligence algorithms, given their ability to continue to function under varying system requirements, thus changing the parameters of the physical and information environment, as well as the emergence of unspecified failures and malfunctions. In the case of artificial intelligence for image classification, specific perturbations, such as adversarial attacks or noise, faults, or fault injection attacks, as well as concept drifts and the presence of out-of-distribution samples, increase aleatoric and epistemic uncertainty, resulting in a decrease in the performance of the intellectual algorithm [1; 2; 3].  
The resilience of the image classifier to perturbations is primarily ensured by achieving robustness in the absorption of a certain level of destructive perturbations and implementing the graceful degradation mechanism to achieve the most effective behavior in conditions of incomplete certainty [1]. Data analysis models need to be continuously improved to take into account the non-stationary environment and new challenges. This is why the ability of the model to quickly recover performance by adapting to destructive effects and improve to increase the efficiency of subsequent adaptations are equally important components of resilience [2]. Recovery and improvement mechanisms are developed within the framework of the continual learning and meta-learning frameworks [4; 5].  
Achieving a certain level of resilience mandates the introduction of a certain resource and functional redundancy into the system, but there are always resource constraints in practice [6]. When designing and operating resilient systems with resource constraints taken into account, the principles of affordable resilience are often used. This involves establishing an effective balance between the system's lifecycle costs and the technical characteristics of its resilience [7]. In the case of biological intelligence, as learning progresses, the cost of decision making decreases. Mechanisms of adaptive computing and cascade or multi-branch models are concerned with achieving a biological-like performance of intelligent algorithms [8; 9].  
Separate components of resilience to certain types of destructive effects have been researched in many scientific papers, but the complex influence of multiple destructive factors at once has still not been considered [1; 2; 3]. In addition, machine learning algorithms for image classification that simultaneously implement components of resilience, such as robustness, graceful degradation, recovery, and improvement, have not yet been proposed. Not all implementations of these components are compatible, especially under resource-constrained conditions. These methodological gaps highlight the need for a research and design of image classification systems with harmonized and resource-efficient implementation of all resilience components to reduce the impact of various kinds of perturbation on classification performance.