Out-domain examples for generative models
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Deep generative models are being increasingly used in a wide variety of applications. However, the generative process is not fully predictable and at times, it produces an unexpected output. We will refer to those outputs as out-domain examples. In the present paper we show that an attacker can force a pre-trained generator to reproduce an arbitrary out-domain example if fed by a suitable adversarial input. The main assumption is that those outputs lie in an unexplored region of the generator's codomain and hence they have a very low probability of being naturally generated. Moreover, we show that this adversarial input can be shaped so as to be statistically indistinguishable from the set of genuine inputs. The goal is to look for an efficient way of finding these inputs in the generator's latent space.
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