In image classification tasks, images are often corrupted by spatial transformationslike translations and rotations. In this work, I utilize an existing method that uses the Fourier series expansion to generate a rotation and translation invariant representation of closed contours found in sketches, aiming to attenuate the effects of distribution shift caused by the aforementioned transformations. I use this technique to transform input images into one of two different invariant representations, a Fourier series representation and a corrected raster image representation, prior to passing them to a neural network for classification. The architectures used include convolutional neutral networks (CNNs), multi-layer perceptrons (MLPs), and graph neural networks (GNNs). I compare the performance of this method to using data augmentation during training, the standard approach for addressing distribution shift, to see which strategy yields the best performance when evaluated against a test set with rotations and translations applied. I include experiments where the augmentations applied during training both do and do not accurately reflect the transformations encountered at test time. Additionally, I investigate the robustness of both approaches to high-frequency noise. In each experiment, I also compare training efficiency across models. I conduct experiments on three data sets, the MNIST handwritten digit dataset, a custom dataset (QD-3) consisting of three classes of geometric figures from the Quick, Draw! hand-drawn sketch dataset, and another custom dataset (QD-345) featuring sketches from all 345 classes found in Quick, Draw!. On the smaller problem space of MNIST and QD-3, the networks utilizing the Fourier-based technique to attenuate distribution shift perform competitively with the standard data augmentation strategy. On the more complex problem space of QD-345, the networks using the Fourier technique do not achieve the same test performance as correctly-applied data augmentation. However, they still outperform instances where train-time augmentations mis-predict test-time transformations, and outperform a naive baseline model where no strategy is used to attenuate distribution shift. Overall, this work provides evidence that strategies which attempt to directly mitigate distribution shift, rather than simply increasing the diversity of the training data, can be successful when certain conditions hold.