Random Projections for Improved Adversarial Robustness
We propose two training techniques for improving the robustness of Neural Networks to adversarial attacks, i.e. manipulations of the inputs that are maliciously crafted to fool networks into incorrect predictions. Both methods are independent of the chosen attack and leverage random projections of the original inputs, with the purpose of exploiting both dimensionality reduction and some characteristic geometrical properties of adversarial perturbations. The first technique is called RP-Ensemble and consists of an ensemble of networks trained on multiple projected versions of the original inputs. The second one, named RP-Regularizer, adds instead a regularization term to the training objective.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial RobustnessDimensionality ReductionSimilar Papers 제목 키워드 기반
Bioinspired random projections for robust, sparse classification
Inspired by the use of random projections in biological sensing systems, we present a new algorithm for processing data in classification problems. This is based on observations of the human brain and the fruit fly's olf…
ClassificationRandom Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks
Advancing loss function design is pivotal for optimizing neural network training and performance. This work introduces Random Linear Projections (RLP) loss, a novel approach that enhances training efficiency by leveragin…
ClassificationRandom forests with random projections of the output space for high dimensional multi-label classification
We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without …
General ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONBinary adaptive embeddings from order statistics of random projections
We use some of the largest order statistics of the random projections of a reference signal to construct a binary embedding that is adapted to signals correlated with such signal. The embedding is characterized from the …
General ClassificationNearest Neighbor Projection Removal Adversarial Training
Deep neural networks have exhibited impressive performance in image classification tasks but remain vulnerable to adversarial examples. Standard adversarial training enhances robustness but typically fails to explicitly …
Adversarial RobustnessImage Classification