## 1 Introduction  
In recent years, machine learning algorithms have been used extensively for image classification tasks. Typically, these tasks are performed by deep neural networks (DNNs) and the related field of science is called deep learning (DL). DNN models generally show substantially higher efficiency than competitive deterministic algorithms. However, the outcomes of DL algorithms depend largely on the quality of the training datasets. The capacity of such a dataset, typically reaching hundreds of thousands of images, determines the ultimate success of the algorithm. Numerous entities construct training datasets, via data gathering and labeling, due to the high value of such datasets in many industries. The majority of publicly available training datasets can only be used in non-commercial applications, with the commercial use of such datasets prohibited. Therefore, commercial entities are generally required to construct their own databases. In these cases, the developed datasets are labeled either automatically or by crowdsourcing [1]. The verification of the label quality is commonly outsourced to a third party. Given that such a third party is often composed of untrained volunteers, the image labeling can be erroneous. [PERSON] et al. [2] showed that labeling quality is one of the largest underlying causes behind the reliability degradation of DL algorithms. This can cause substantial problems in the deployment of DL algorithms in safety-critical applications. The large number of safety standards for artificial intelligence algorithms indicates the need to evaluate the quality of training dataset labeling. For example, ISO/TR 4804:2020 and ISO/DIS 21448 both indicate that labeling should be reviewed. However, no widely accepted procedures governing label review exist. In practice, labeling noise is rarely evaluated. It is typically assumed that the data are either perfect or have a negligible error rate. However, the error rate in popular databases, including verification datasets, can be as high as 10% [3]. This can significantly reduce the maximum accuracy of the corresponding DL algorithms. Moreover, such a high proportion of mislabeling implies that such algorithms should not be expected to reach 100% accuracy. The problem of labeling noise has been addressed by researchers in recent years. The simplest method to evaluate the quality of a training dataset is to fully label the same set multiple times using different annotators. For each image, the labels of each annotator can then be compared to detect inaccuracies. The level of agreement between a fixed number of annotators when assigning categorical labels can be measured using inter-annotator agreement metrics [4]. However, this substantially increases preparation cost, as the dataset must be annotated multiple times. Fully automated approaches to noisy label detection also exist. Such works, however, do not attempt to evaluate the number of erroneous labels detected but instead seek to implement adaptive, noise-tolerant DL algorithms. Thus, these solutions are only appropriate for the training of DL algorithms on noisy datasets and cannot be used to verify the accuracy of training datasets provided by an external supplier. [PERSON] et al. [5] modified the loss function to provide a reduced penalty when an image label is suspected to be incorrect. According to the authors, this solution also allows the use of databases in which not all labels are known. [PERSON] and [PERSON] [6] added an additional softmax layer to the DNN model, creating an S-Model architecture. The authors assume that the dataset noise has a certain distribution, which is modeled in their proposed solution. This approach uses the expectation-maximization algorithm. [PERSON] et al. [7] presented co-teaching. This method uses two DNNs which, by exchanging data, decide if an image sample is incorrectly labeled. Both of the cooperating DNNs must make the same decision for a label to be judged as inaccurate. The MentorNet network proposed by [PERSON] et al.