Title: FoveaTer: Foveated Transformer for Image Classification. Abstract: Many animals and humans process the visual field with varying spatial resolution (foveated vision) and use peripheral processing to make eye movements and point the fovea to acquire high-resolution information about objects of interest. This architecture results in computationally efficient rapid scene exploration. Recent progress in vision Transformers has brought about new alternatives to the traditionally convolution-reliant computer vision systems. However, the Transformer models do not explicitly model the foveated properties of the visual system nor the interaction between eye movements and the classification task. We propose foveated Transformer (FoveaTer) model, which uses pooling regions and eye movements to perform object classification tasks using a vision Transformer architecture. Our proposed model pools the image features using squared pooling regions, an approximation to the biologically-inspired foveated architecture, and uses the pooled features as an input to a Transformer Network. It decides on subsequent fixation locations based on the attention assigned by the Transformer to various locations from previous and present fixations. The model uses a confidence threshold to stop scene exploration, dynamically allocating more fixation/computational resources to more challenging images. After reaching the stopping criterion, the model makes the final object category decision. We construct a Foveated model using our proposed approach and compare it against a Full-resolution model, which does not contain any pooling. On the ImageNet-100 dataset, our Foveated model achieves the accuracy of the Full-resolution model using only 35% transformer computations and 73% overall computations. Finally, we demonstrate our model's robustness against adversarial attacks, where it outperforms the full-resolution model.