Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers

29 Jul 2020Jayaram RaghuramVarun ChandrasekaranSomesh JhaSuman Banerjee

Detecting anomalous inputs, such as adversarial and out-of-distribution (OOD) inputs, is critical for classifiers deployed in real-world applications, especially deep neural network (DNN) classifiers that are known to be brittle on such inputs. We propose an unsupervised statistical testing framework for detecting such anomalous inputs to a trained DNN classifier based on its internal layer representations... (read more)

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