Results 61 to 70 of about 6,306,959 (200)
A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
Applied Sciences, 2021 Transfer learning using pre-trained deep neural networks (DNNs) has been widely used for plant disease identification recently. However, pre-trained DNNs are susceptible to adversarial attacks which generate adversarial samples causing DNN models to make
Zhirui Luo, Qingqing Li, Jun Zheng
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Semantic Adversarial Deep Learning [PDF]
IEEE Design & Test, 2018 Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection.
Sanjit A. Seshia +2 more
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, 2017 <p>This repository presents a new adversarial learning method for generative conversational agents (GCA) besides a new model of GCA. Our method assumes the GCA as a generator that aims at fooling a discriminator that labels dialogues as human ...
Oswaldo Ludwig
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Adversarial Robustness on Image Classification With
IEEE AccessAttacks and defences in adversarial machine learning literature have primarily focused on supervised learning. However, it remains an open question whether existing methods and strategies can be adapted to unsupervised learning approaches.
Rollin Omari, Junae Kim, Paul Montague
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Decomposed Adversarial Learned Inference
CoRR, 2020 Effective inference for a generative adversarial model remains an important and challenging problem. We propose a novel approach, Decomposed Adversarial Learned Inference (DALI), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on the dependency structure of the generative model. We
Alexander Hanbo Li +3 more
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Adversarial Machine Learning [PDF]
, 2022 Recent innovations in machine learning enjoy a remarkable rate of adoption across a broad spectrum of applications, including cyber-security. While previous chapters study the application of machine learning solutions to cyber-security, in this chapter ...
Serban, A.C. +8 more
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Adversarial Sample Detection in Computer Vision:A Survey [PDF]
Jisuanji kexueWith the increase in data volume and improvement in hardware performance,deep learning(DL) has made significant progress in the field of computer vision.However,deep learning models are vulnerable to adversarial samples,causing significant changes in the
ZHANG Xin, ZHANG Han, NIU Manyu, JI Lixia
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Meta-Learning Adversarial Bandits
CoRR, 2022 19 ...
Maria-Florina Balcan +3 more
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Politics of Adversarial Machine Learning [PDF]
SSRN Electronic Journal, 2020 In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights.
Kendra Albert +3 more
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Targeted Adversarial Learning Optimized Sampling [PDF]
, 2019 Boosting transitions of rare events is critical to modern-day simulations of complex dynamic systems. We present a novel approach to modify the potential energy surface in order to drive the system to a user-defined target distribution where the free ...
Jun, Zhang, Yi Isaac, Yang, Frank, Noé
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