  Autonomous systems are highly vulnerable to a variety of adversarial attacks
on Deep Neural Networks (DNNs). Training-free model-agnostic defenses have
recently gained popularity due to their speed, ease of deployment, and ability
to work across many DNNs. To this end, a new technique has emerged for
mitigating attacks on image classification DNNs, namely, preprocessing
adversarial images using super resolution -- upscaling low-quality inputs into
high-resolution images. This defense requires running both image classifiers
and super resolution models on constrained autonomous systems. However, super
resolution incurs a heavy computational cost. Therefore, in this paper, we
investigate the following question: Does the robustness of image classifiers
suffer if we use tiny super resolution models? To answer this, we first review
a recent work called Super-Efficient Super Resolution (SESR) that achieves
similar or better image quality than prior art while requiring 2x to 330x fewer
Multiply-Accumulate (MAC) operations. We demonstrate that despite being orders
of magnitude smaller than existing models, SESR achieves the same level of
robustness as significantly larger networks. Finally, we estimate end-to-end
performance of super resolution-based defenses on a commercial Arm Ethos-U55
micro-NPU. Our findings show that SESR achieves nearly 3x higher FPS than a
baseline while achieving similar robustness.
