  Full-precision deep learning models are typically too large or costly to
deploy on edge devices. To accommodate to the limited hardware resources,
models are adapted to the edge using various edge-adaptation techniques, such
as quantization and pruning. While such techniques may have a negligible impact
on top-line accuracy, the adapted models exhibit subtle differences in output
compared to the original model from which they are derived. In this paper, we
introduce a new evasive attack, DIVA, that exploits these differences in edge
adaptation, by adding adversarial noise to input data that maximizes the output
difference between the original and adapted model. Such an attack is
particularly dangerous, because the malicious input will trick the adapted
model running on the edge, but will be virtually undetectable by the original
model, which typically serves as the authoritative model version, used for
validation, debugging and retraining. We compare DIVA to a state-of-the-art
attack, PGD, and show that DIVA is only 1.7-3.6% worse on attacking the adapted
model but 1.9-4.2 times more likely not to be detected by the the original
model under a whitebox and semi-blackbox setting, compared to PGD.
