> Different amounts of necessary accuracy lead to completely different model architectures Not really. For object detection, there are really only two kinds o

> Different amounts of necessary accuracy lead to completely different model architectures Not really. For object detection, there are really only two kinds of architecture in common use - the multi-stage RCNN-style and the single-pass YOLO style. YOLO's are much faster, and not as accurate. But within each of these architectures, there's a big knob you can turn to trade-off speed vs accuracy. But the incredible truth is that those speed/accuracy trade-offs don't matter at all when facing an adversarial attack like this. The attacks will work reliably very well, as long as there are enough pixels and the t-shirt or whatever is facing the right direction, regardless of how the model is tuned for speed vs accuracy. That is, if you know the dataset it was trained on.