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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
The DeepFool algorithm is proposed to efficiently compute perturbations that fool deep networks, and thus reliably quantify the robustness of these classifiers, and outperforms recent methods in the task of computing adversarial perturbation and making classifiers more robust. Expand
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
This tutorial overview outlines the main challenges of the emerging field of signal processing on graphs, discusses different ways to define graph spectral domains, which are the analogs to the classical frequency domain, and highlights the importance of incorporating the irregular structures of graph data domains when processing signals on graphs. Expand
Universal Adversarial Perturbations
The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers and outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images. Expand
Graph Signal Processing: Overview, Challenges, and Applications
An overview of core ideas in GSP and their connection to conventional digital signal processing are provided, along with a brief historical perspective to highlight how concepts recently developed build on top of prior research in other areas. Expand
Learning Laplacian Matrix in Smooth Graph Signal Representations
This paper addresses the problem of learning graph Laplacians, which is equivalent to learning graph topologies, such that the input data form graph signals with smooth variations on the resulting topology and proposes an algorithm for learning graphs that enforces such property and is based on minimizing the variations of the signals on the learned graph. Expand
Dictionary Learning
We describe methods for learning dictionaries that are appropriate for the representation of given classes of signals and multisensor data. We further show that dimensionality reduction based onExpand
Video Packet Selection and Scheduling for Multipath Streaming
Simulation results demonstrate that the proposed scheduling solution performs better than common scheduling algorithms, and therefore represents a very efficient low-complexity multipath streaming algorithm, for both stored and live video services. Expand
Robustness of classifiers: from adversarial to random noise
This paper proposes the first quantitative analysis of the robustness of nonlinear classifiers in this general noise regime, and establishes precise theoretical bounds on the robustity of classifier's decision boundary, which depend on the curvature of the classifiers' decision boundary. Expand
Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds
This paper addresses the problem of analyzing multi-layer graphs and proposes methods for clustering the vertices by efficiently merging the information provided by the multiple modalities to combine the characteristics of individual graph layers using tools from subspace analysis on a Grassmann manifold. Expand
Analysis of classifiers’ robustness to adversarial perturbations
A general upper bound on the robustness of classifiers to adversarial perturbations is established, and the phenomenon of adversarial instability is suggested to be due to the low flexibility ofclassifiers, compared to the difficulty of the classification task (captured mathematically by the distinguishability measure). Expand