584 research outputs found
Robust Adversarial Defense by Tensor Factorization
As machine learning techniques become increasingly prevalent in data
analysis, the threat of adversarial attacks has surged, necessitating robust
defense mechanisms. Among these defenses, methods exploiting low-rank
approximations for input data preprocessing and neural network (NN) parameter
factorization have shown potential. Our work advances this field further by
integrating the tensorization of input data with low-rank decomposition and
tensorization of NN parameters to enhance adversarial defense. The proposed
approach demonstrates significant defense capabilities, maintaining robust
accuracy even when subjected to the strongest known auto-attacks. Evaluations
against leading-edge robust performance benchmarks reveal that our results not
only hold their ground against the best defensive methods available but also
exceed all current defense strategies that rely on tensor factorizations. This
study underscores the potential of integrating tensorization and low-rank
decomposition as a robust defense against adversarial attacks in machine
learning.Comment: Accepted at 2023 ICMLA Conferenc
Robustness of 3D Deep Learning in an Adversarial Setting
Understanding the spatial arrangement and nature of real-world objects is of
paramount importance to many complex engineering tasks, including autonomous
navigation. Deep learning has revolutionized state-of-the-art performance for
tasks in 3D environments; however, relatively little is known about the
robustness of these approaches in an adversarial setting. The lack of
comprehensive analysis makes it difficult to justify deployment of 3D deep
learning models in real-world, safety-critical applications. In this work, we
develop an algorithm for analysis of pointwise robustness of neural networks
that operate on 3D data. We show that current approaches presented for
understanding the resilience of state-of-the-art models vastly overestimate
their robustness. We then use our algorithm to evaluate an array of
state-of-the-art models in order to demonstrate their vulnerability to
occlusion attacks. We show that, in the worst case, these networks can be
reduced to 0% classification accuracy after the occlusion of at most 6.5% of
the occupied input space.Comment: 10 pages, 8 figures, 1 tabl
Robust Deep Networks with Randomized Tensor Regression Layers
In this paper, we propose a novel randomized tensor decomposition for tensor regression. It allows to stochastically approximate the weights of tensor regression layers by randomly sampling in the low-rank subspace. We theoretically and empirically establish the link between our proposed stochastic rank-regularization and the dropout on low-rank tensor regression. This acts as an additional stochastic regularization on the regression weight, which, combined with the deterministic regularization imposed by the low-rank constraint, improves both the performance and robustness of neural networks augmented with it. In particular, it makes the model more robust to adversarial attacks and random noise, without requiring any adversarial training. We perform a thorough study of our method on synthetic data, object classification on the CIFAR100 and ImageNet datasets, and large scale brain-age prediction on UK Biobank brain MRI dataset. We demonstrate superior performance in all cases, as well as significant improvement in robustness to adversarial attacks and random noise
Improving Robustness of Deep Convolutional Neural Networks via Multiresolution Learning
The current learning process of deep learning, regardless of any deep neural
network (DNN) architecture and/or learning algorithm used, is essentially a
single resolution training. We explore multiresolution learning and show that
multiresolution learning can significantly improve robustness of DNN models for
both 1D signal and 2D signal (image) prediction problems. We demonstrate this
improvement in terms of both noise and adversarial robustness as well as with
small training dataset size. Our results also suggest that it may not be
necessary to trade standard accuracy for robustness with multiresolution
learning, which is, interestingly, contrary to the observation obtained from
the traditional single resolution learning setting
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