###### Abstract  
Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive datasets. However, obtaining these datasets poses significant challenges due to patient privacy and data collection restrictions. Additive data augmentation, which enhances data variability, emerges as a promising solution. Yet, it's unclear which augmentation techniques are most effective for KOA. Our study explored data augmentation methods, including adversarial techniques. We used strategies like horizontal cropping and region of interest (ROI) extraction, alongside adversarial methods such as noise injection and ROI removal. Interestingly, rotations improved performance, while methods like horizontal split were less effective. We discovered potential confounding regions using adversarial augmentation, shown in our models' accurate classification of extreme KOA grades, even without the knee joint. This indicated a potential model bias towards irrelevant radiographic features. Removing the knee joint paradoxically increased accuracy in classifying early-stage KOA. Grad-CAM visualizations helped elucidate these effects. Our study contributed to the field by pinpointing augmentation techniques that either improve or impede model performance, in addition to recognizing potential confounding regions within radiographic images of knee osteoarthritis. knee joint osteoarthritis (KOA); global disability; data augmentation; technique selection; data variability; deep learning; transfer learning; adversarial learning; adversarial augmentation Article Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Redatasets. However, obtaining such datasets is challenging, constrained by patient privacy considerations [35; 36], data collection restrictions, and the inherent nature of OA. Various studies have utilized data augmentation techniques as a workaround, creating artificial data variability. For KOA, two primary data augmentation methods are employed: affine and online, where random transformations occur during training, and additive and offline, manipulating the base (original) data before training to generate more data points. These techniques, often used in tandem, have been successful in enhancing performance and mitigating overfitting. However, there has been no systematic exploration to determine which technique is most effective for the task at hand, nor which might be less beneficial. There has been a notable gap in research regarding the impact of augmentation on medical data, especially in the context of knee joint osteoarthritis (KOA). While existing studies [37; 38] in other medical imaging areas have identified both beneficial and detrimental effects of specific augmentations. Existing KOA studies have employed various approaches [39; 40; 31], but without focusing on base data augmentation as the research objective. To the best of our knowledge, our study is the first to investigate both positive and adversarial augmentations in the context of knee joint osteoarthritis and first to explore adversarial augmentation beyond noise injection seen in prior medical imaging work. Positive augmentations, a subset of offline base data augmentations, involve modifications that supportively enhance the dataset. These include variations of the original images and mild transformations that preserve the data's core characteristics. Adversarial augmentations, on the other hand, introduce substantial alterations, such as noise and complex distortions, to challenge the model under difficult conditions. These augmentations are crucial for evaluating the model's sensitivity and robustness, exposing its performance limitations and areas needing improvement. They can play a significant role in helping to identify confounds within images, clarifying which aspects of the data the model might rely on.