Robustness of AI Models to Synthetic Data in Color Image Classification: A Comparative Analysis

This study analyzed how the performance of various neural network architectures responds to offline image augmentation using standard geometric and photometric transformations. We evaluated a composite training-data enrichment protocol using two multiclass color image classification tasks: a dataset of leg photographs classified according to the CEAP system (sourced from open-source repositories; 20,971 images, seven classes) and a dermatological image dataset (6786 images, eight classes). To enrich the initially balanced subsets, transformed images were added to classes underrepresented in the original datasets, while additional real images were added to the remaining classes. Twelve architectures were investigated, including classical CNNs, vision transformers, and a modernized CNN. Across the 12 architectures, 11 augmentation levels, and 20 runs per configuration, mean accuracy ranged from 0.55 to 0.68 (leg photographs) and from 0.59 to 0.79 (dermatological images). The expansion levels yielding the highest observed mean accuracy varied across configurations, with descriptive maxima occurring at levels from 0% to 100%. For each model-dataset configuration, the highest mean accuracy across the eleven levels was treated as a descriptive maximum. Standard deviations across runs characterized the variability of the training process, whereas standard errors reflected the uncertainty of mean estimates given a fixed test set. Transformer-based configurations demonstrated lower average run-to-run standard deviations; however, this tendency was configuration- and dataset-dependent, warranting further research. An exploratory augmentation-as-biased-variance-reduction (ABVR) model is proposed to describe the observed accuracy curves. Its fitted coefficients and maximizers summarize the available data and have not been independently validated.