Integrating single-shot Fast Gradient Sign Method (FGSM) with classical image processing techniques for generating adversarial attacks on deep learning classifiers-Reference-Cited by-同舟云学术

Integrating single-shot Fast Gradient Sign Method (FGSM) with classical image processing techniques for generating adversarial attacks on deep learning classifiers

Published:2022-03-05 Issue: Volume: Page:
ISSN:
Container-title:Fourteenth International Conference on Machine Vision (ICMV 2021)
language:
Short-container-title:

Author:

Hassan Muhammad,Younis Shahzad,Rasheed Ahmed,Bilal Muhammad

Publisher

SPIE

Reference31 articles.

1. Dermatologist-level classification of skin cancer with deep neural networks

2. End to end learning for self-driving cars;Bojarski;arXiv preprint arXiv:1604.07316,2016

3. Sequence to sequence learning with neural networks;Sutskever;In Advances in neural information processing systems,2014

4. Intriguing properties of neural networks;Szegedy;arXiv preprint arXiv:1312.6199,2013

5. Detecting adversarial image examples in deep neural networks with adaptive noise reduction;Liang;IEEE Transactions on Dependable and Secure Computing,2018

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Generating and Defending Against Adversarial Examples for Loan Eligibility Prediction;2023 International Conference on System, Computation, Automation and Networking (ICSCAN);2023-11-17

2. Breaking classification Algorithms: A Black-Box Adversarial Attack for Data Poisoning;2023 International Conference on Innovations in Engineering and Technology (ICIET);2023-07-13

3. Securing ResNet50 against Adversarial Attacks: Evasion and Defense using BIM Algorithm;2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS);2023-05-17