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In a model inversion attack, an adversary attempts to reconstruct the datarecords, used to train a target model, using only the model's output. Inlaunching a contemporary model inversion attack, the strategies discussed aregenerally based on either predicted confidence score vectors, i.e., black-boxattacks, or the parameters of a target model, i.e., white-box attacks. However,in the real world, model owners usually only give out the predicted labels; theconfidence score vectors and model parameters are hidden as a defense mechanismto prevent such attacks. Unfortunately, we have found a model inversion methodthat can reconstruct the input data records based only on the output labels. Webelieve this is the attack that requires the least information to succeed and,therefore, has the best applicability. The key idea is to exploit the errorrate of the target model to compute the median distance from a set of datarecords to the decision boundary of the target model. The distance, then, isused to generate confidence score vectors which are adopted to train an attackmodel to reconstruct the data records. The experimental results show thathighly recognizable data records can be reconstructed with far less informationthan existing methods.

Label-only Model Inversion Attack: The Attack that Requires the Least Information