  As deep neural networks (DNNs) become widely used, pruned and quantised
models are becoming ubiquitous on edge devices; such compressed DNNs are
popular for lowering computational requirements. Meanwhile, recent studies show
that adversarial samples can be effective at making DNNs misclassify. We,
therefore, investigate the extent to which adversarial samples are transferable
between uncompressed and compressed DNNs. We find that adversarial samples
remain transferable for both pruned and quantised models. For pruning, the
adversarial samples generated from heavily pruned models remain effective on
uncompressed models. For quantisation, we find the transferability of
adversarial samples is highly sensitive to integer precision.
