Results 101 to 110 of about 1,662,189 (295)

Adversarial Robustness by One Bit Double Quantization for Visual Classification

IEEE Access, 2019
In this paper, we propose a novel robust visual classification framework that uses double quantization (dquant) to defend against adversarial examples in a specific attack scenario called “subsequent adversarial examples” where test images ...
Maungmaung Aprilpyone, Yuma Kinoshita, Hitoshi Kiya   +2 more
doaj   +1 more source

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Financial Development in Adversarial and Inquisitorial Legal Systems [PDF]

This paper analyzes how the adversarial and inquisitorial evidence collection procedures affect financial development. In investigating the true returns of insolvent entrepreneurs, the adversarial procedure relies on lawyers whereas the inquisitorial ...
Massenot Baptiste
core  

Adversarial Risk Análysis for Counterterrorism Modelling [PDF]

, 2013
Recent large scale terrorist attacks have raised interest in models for resource allocation against terrorist threats. The unifying theme in this area is the need to develop methods for the analysis of allocation decisions when risks stem from the ...
Ríos, Jesús, Ríos Insúa, David
core  

Hessian-Free Second-Order Adversarial Examples for Adversarial Learning

, 2022
Recent studies show deep neural networks (DNNs) are extremely vulnerable to the elaborately designed adversarial examples. Adversarial learning with those adversarial examples has been proved as one of the most effective methods to defend against such an
Wang, Yuqi, Guo, Yuhan, Swaileh, Wassim, Qian, Yaguan, Gu, Zhaoquan, Wang, Bin   +5 more
core  

Provably Robust Adversarial Examples

, 2020
International Conference on Learning Representations (ICLR 2022)
Dimitar Iliev Dimitrov, Gagandeep Singh 0001, Timon Gehr, Martin T. Vechev   +3 more
openaire   +4 more sources

A New Kind of Adversarial Example

CoRR, 2022
Almost all adversarial attacks are formulated to add an imperceptible perturbation to an image in order to fool a model. Here, we consider the opposite which is adversarial examples that can fool a human but not a model. A large enough and perceptible perturbation is added to an image such that a model maintains its original decision, whereas a human ...
openaire   +2 more sources

Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning

IEEE Access
Recent advances in adversarial machine learning have shown that defenses previously considered robust are actually susceptible to adversarial attacks which are specifically customized to target their weaknesses.
Kaleel Mahmood, Ethan Rathbun, Ronak Sahu, Marten Van Dijk, Sohaib Ahmad, Caiwen Ding   +5 more
doaj   +1 more source

Image Classification Adversarial Example Defense Method Based on Conditional Diffusion Model [PDF]

Jisuanji gongcheng
Deep-learning models have achieved impressive results in fields such as image classification; however, they remain vulnerable to interference and threats from adversarial examples.
CHEN Zimin, GUAN Zhitao
doaj   +1 more source

Structural Divergence Between the Moltbook AI‐Agent Network and Human Social Networks

Advanced Science, EarlyView.
Analysis of the Moltbook AI‐agent network reveals a striking combination of familiar global scaling and distinct internal organization. Attention is highly concentrated, reciprocity is limited, connected triads are suppressed, and communities are strongly modular.
Wenpin Hou, Zhicheng Ji
wiley   +1 more source