  Out-of-distribution (OOD) detection aims to identify OOD data based on
representations extracted from well-trained deep models. However, existing
methods largely ignore the reprogramming property of deep models and thus may
not fully unleash their intrinsic strength: without modifying parameters of a
well-trained deep model, we can reprogram this model for a new purpose via
data-level manipulation (e.g., adding a specific feature perturbation to the
data). This property motivates us to reprogram a classification model to excel
at OOD detection (a new task), and thus we propose a general methodology named
watermarking in this paper. Specifically, we learn a unified pattern that is
superimposed onto features of original data, and the model's detection
capability is largely boosted after watermarking. Extensive experiments verify
the effectiveness of watermarking, demonstrating the significance of the
reprogramming property of deep models in OOD detection.
