Adversary Detection in Neural Networks via Persistent Homology
Adversary Detection in Neural Networks via Persistent Homology
We outline a detection method for adversarial inputs to deep neural networks. By viewing neural network computations as graphs upon which information flows from input space to out- put distribution, we compare the differences in graphs induced by different inputs. Specifically, by applying persistent homology to these induced graphs, we observe that the structure of the most persistent subgraphs which generate the first homology group differ between adversarial and unperturbed inputs. Based on this observation, we build a detection algorithm that depends only on the topological information extracted during training. We test our algorithm on MNIST and achieve 98% detection adversary accuracy with F1-score 0.98.
Thomas Gebhart、Paul Schrater
计算技术、计算机技术
Thomas Gebhart,Paul Schrater.Adversary Detection in Neural Networks via Persistent Homology[EB/OL].(2017-11-27)[2025-07-23].https://arxiv.org/abs/1711.10056.点此复制
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