Related papers: Tropical Decision Boundaries for Neural Networks Are Robust Against Adversarial Attacks

Tropical Decision Boundaries for Neural Networks Are Robust Against Adversarial Attacks

URL: http://arxiv.org/abs/2402.00576v1
Date: Thu, 1 Feb 2024 13:14:38 GMT
Title: Tropical Decision Boundaries for Neural Networks Are Robust Against Adversarial Attacks
Authors: Kurt Pasque and Christopher Teska and Ruriko Yoshida and Keiji Miura and Jefferson Huang
Abstract summary: We exploit the tropical nature of piece-wise linear neural networks by embedding the data in the tropical projective torus in a single hidden layer which can be added to any model. We show its robustness against adversarial attacks on image datasets using computational experiments.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We introduce a simple, easy to implement, and computationally efficient tropical convolutional neural network architecture that is robust against adversarial attacks. We exploit the tropical nature of piece-wise linear neural networks by embedding the data in the tropical projective torus in a single hidden layer which can be added to any model. We study the geometry of its decision boundary theoretically and show its robustness against adversarial attacks on image datasets using computational experiments.

Related papers

Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks [0.16385815610837165]
Sparse deep learning addresses challenges by recovering a sparse representation of the underlying target function. Deep neural architectures compressed via structured sparsity provide low latency inference, higher data throughput, and reduced energy consumption. We propose structurally sparse Bayesian neural networks which prune excessive nodes with (i) Spike-and-Slab Group Lasso (SS-GL), and (ii) Spike-and-Slab Group Horseshoe (SS-GHS) priors.
arXiv Detail & Related papers (2023-08-17T17:14:18Z)
Quiver neural networks [5.076419064097734]
We develop a uniform theoretical approach towards the analysis of various neural network connectivity architectures. Inspired by quiver representation theory in mathematics, this approach gives a compact way to capture elaborate data flows.
arXiv Detail & Related papers (2022-07-26T09:42:45Z)
An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
arXiv Detail & Related papers (2022-04-18T23:42:18Z)
FuNNscope: Visual microscope for interactively exploring the loss landscape of fully connected neural networks [77.34726150561087]
We show how to explore high-dimensional landscape characteristics of neural networks. We generalize observations on small neural networks to more complex systems. An interactive dashboard opens up a number of possible application networks.
arXiv Detail & Related papers (2022-04-09T16:41:53Z)
Pruning in the Face of Adversaries [0.0]
We evaluate the impact of neural network pruning on the adversarial robustness against L-0, L-2 and L-infinity attacks. Our results confirm that neural network pruning and adversarial robustness are not mutually exclusive. We extend our analysis to situations that incorporate additional assumptions on the adversarial scenario and show that depending on the situation, different strategies are optimal.
arXiv Detail & Related papers (2021-08-19T09:06:16Z)
Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
arXiv Detail & Related papers (2021-03-18T13:04:21Z)
Improving Neural Network Robustness through Neighborhood Preserving Layers [0.751016548830037]
We demonstrate a novel neural network architecture which can incorporate such layers and also can be trained efficiently. We empirically show that our designed network architecture is more robust against state-of-art gradient descent based attacks.
arXiv Detail & Related papers (2021-01-28T01:26:35Z)
Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
arXiv Detail & Related papers (2020-12-17T15:19:29Z)
An Ode to an ODE [78.97367880223254]
We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the group O(d) This nested system of two flows provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem.
arXiv Detail & Related papers (2020-06-19T22:05:19Z)
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness [97.67477497115163]
We use mode connectivity to study the adversarial robustness of deep neural networks. Our experiments cover various types of adversarial attacks applied to different network architectures and datasets. Our results suggest that mode connectivity offers a holistic tool and practical means for evaluating and improving adversarial robustness.
arXiv Detail & Related papers (2020-04-30T19:12:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.