  The representations of the activation space of deep neural networks (DNNs)
are widely utilized for tasks like natural language processing, anomaly
detection and speech recognition. Due to the diverse nature of these tasks and
the large size of DNNs, an efficient and task-independent representation of
activations becomes crucial. Empirical p-values have been used to quantify the
relative strength of an observed node activation compared to activations
created by already-known inputs. Nonetheless, keeping raw data for these
calculations increases memory resource consumption and raises privacy concerns.
To this end, we propose a model-agnostic framework for creating representations
of activations in DNNs using node-specific histograms to compute p-values of
observed activations without retaining already-known inputs. Our proposed
approach demonstrates promising potential when validated with multiple network
architectures across various downstream tasks and compared with the kernel
density estimates and brute-force empirical baselines. In addition, the
framework reduces memory usage by 30% with up to 4 times faster p-value
computing time while maintaining state of-the-art detection power in downstream
tasks such as the detection of adversarial attacks and synthesized content.
Moreover, as we do not persist raw data at inference time, we could potentially
reduce susceptibility to attacks and privacy issues.
