Empirical Review of Traditional and Recent Balancing Techniques for Image-Based Physical Violence Classification Across Diverse Imbalance Scenarios and Multiple Datasets

The automated classification of physical violence in images faces a critical methodological obstacle: class imbalance, which undermines the discriminative capacity of Deep Learning (DL) models by leading them to overfit the majority class. Although numerous balancing strategies have been proposed in the literature, which range from traditional resampling to advanced generative models, a systematic and controlled empirical evaluation of their behavior in the specific domain of physical violence detection remains absent. In this study, we present a systematic empirical review that evaluates traditional balancing techniques, generative models, and data augmentation methods under controlled imbalance conditions. The methodology is applied to five datasets (UBI-FIGHTS, RLVSD, RWF-2000, RLVS, and AIRTLAB), across six imbalance scenarios defined by Balance Percentage levels of 1%, 5%, 10%, 25%, 50%, and 75%. To isolate the effect of the balancing strategies from architectural confounders, a single stable backbone (ResNet-18) is employed as a controlled classifier across all experimental conditions. The experimental results demonstrate the absence of a universally optimal balancing technique: in highly imbalanced scenarios, traditional oversampling methods such as ROS achieved the best average performance, while generative approaches such as DCGAN and DeepSMOTE became increasingly competitive as class balance is improved. These findings confirm that the effectiveness of balancing techniques depends on both the degree of asymmetry and the dataset’s intrinsic characteristics. Thus, it is clear that there is a need for context-aware strategy selection rather than one-size-fits-all solutions. Beyond the empirical findings, this work provides a structured synthesis of the theoretical foundations and state-of-the-art methods for imbalanced violence detection.