Machine learning (ML) models are costly to train as they can require a
significant amount of data, computational resources and technical expertise.
Thus, they constitute valuable intellectual property that needs protection from
adversaries wanting to steal them. Ownership verification techniques allow the
victims of model stealing attacks to demonstrate that a suspect model was in
fact stolen from theirs. Although a number of ownership verification techniques
based on watermarking or fingerprinting have been proposed, most of them fall
short either in terms of security guarantees (well-equipped adversaries can
evade verification) or computational cost. A fingerprinting technique
introduced at ICLR ’21, Dataset Inference (DI), has been shown to offer better
robustness and efficiency than prior methods. The authors of DI provided a
correctness proof for linear (suspect) models. However, in the same setting, we
prove that DI suffers from high false positives (FPs) — it can incorrectly
identify an independent model trained with non-overlapping data from the same
distribution as stolen. We further prove that DI also triggers FPs in
realistic, non-linear suspect models. We then confirm empirically that DI leads
to FPs, with high confidence. Second, we show that DI also suffers from false
negatives (FNs) — an adversary can fool DI by regularising a stolen model’s
decision boundaries using adversarial training, thereby leading to an FN. To
this end, we demonstrate that DI fails to identify a model adversarially
trained from a stolen dataset — the setting where DI is the hardest to evade.
Finally, we discuss the implications of our findings, the viability of
fingerprinting-based ownership verification in general, and suggest directions
for future work.

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