English

Adversarial Vision Challenge

Machine Learning 2018-12-07 v2 Computer Vision and Pattern Recognition Machine Learning
Authors: Wieland Brendel , Jonas Rauber , Alexey Kurakin , Nicolas Papernot , Behar Veliqi , Marcel Salathé , Sharada P. Mohanty , Matthias Bethge
View on arXiv ↗ PDF ↗

Abstract

The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural Information Processing Systems (NIPS 2018).

Keywords

image processing competition adversarial examples

Cite

@article{arxiv.1808.01976,
  title  = {Adversarial Vision Challenge},
  author = {Wieland Brendel and Jonas Rauber and Alexey Kurakin and Nicolas Papernot and Behar Veliqi and Marcel Salathé and Sharada P. Mohanty and Matthias Bethge},
  journal= {arXiv preprint arXiv:1808.01976},
  year   = {2018}
}

Comments

https://www.crowdai.org/challenges/adversarial-vision-challenge

Related papers

View all related →
Computer Vision and Pattern Recognition · Computer Science
Adversarial Attacks and Defences Competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong +19
2018-04-03
Computer Vision and Pattern Recognition · Computer Science
Adversarial Attacks on ML Defense Models Competition
Yinpeng Dong, Qi-An Fu, Xiao Yang, Wenzhao Xiang +24
2021-10-18
Computer Vision and Pattern Recognition · Computer Science
A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges
Nandish Chattopadhyay, Abdul Basit, Bassem Ouni, Muhammad Shafique
2025-03-04
Machine Learning · Computer Science
CAAD 2018: Generating Transferable Adversarial Examples
Yash Sharma, Tien-Dung Le, Moustafa Alzantot
2018-11-22
Cryptography and Security · Computer Science
Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects
Sabrine Ennaji, Fabio De Gaspari, Dorjan Hitaj, Alicia Kbidi +1
2024-10-23
Computer Vision and Pattern Recognition · Computer Science
CAAD 2018: Iterative Ensemble Adversarial Attack
Jiayang Liu, Weiming Zhang, Nenghai Yu
2018-11-11
Computer Vision and Pattern Recognition · Computer Science
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Naveed Akhtar, Ajmal Mian
2018-02-27
Cryptography and Security · Computer Science
Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge
Deqiang Li, Qianmu Li, Yanfang Ye, Shouhuai Xu
2020-09-17
Computer Vision and Pattern Recognition · Computer Science
Unrestricted Adversarial Attacks on ImageNet Competition
Yuefeng Chen, Xiaofeng Mao, Yuan He, Hui Xue +34
2021-10-27
Machine Learning · Computer Science
The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection
Harm Berntsen, Wouter Kuijper, Tom Heskes
2016-07-15
Cryptography and Security · Computer Science
Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges
Bowei Xi
2021-07-08
Computer Vision and Pattern Recognition · Computer Science
Benchmarking Adversarial Robustness
Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang +3
2019-12-30
Computer Vision and Pattern Recognition · Computer Science
A Survey on Physical Adversarial Attack in Computer Vision
Donghua Wang, Wen Yao, Tingsong Jiang, Guijian Tang +1
2023-09-19
Computer Vision and Pattern Recognition · Computer Science
When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture
Yichuan Mo, Dongxian Wu, Yifei Wang, Yiwen Guo +1
2022-10-17
Computer Vision and Pattern Recognition · Computer Science
The Human Visual System and Adversarial AI
Yaoshiang Ho, Samuel Wookey
2020-01-09
Machine Learning · Computer Science
Adversarial attacks hidden in plain sight
Jan Philip Göpfert, André Artelt, Heiko Wersing, Barbara Hammer
2020-04-28
Machine Learning · Computer Science
Bridging Adversarial Robustness and Gradient Interpretability
Beomsu Kim, Junghoon Seo, Taegyun Jeon
2019-04-22
Computer Vision and Pattern Recognition · Computer Science
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey
Abhijith Sharma, Yijun Bian, Phil Munz, Apurva Narayan
2022-06-17
Computer Vision and Pattern Recognition · Computer Science
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective
Gabriel Resende Machado, Eugênio Silva, Ronaldo Ribeiro Goldschmidt
2021-11-30
Computer Vision and Pattern Recognition · Computer Science
Deep Defense: Training DNNs with Improved Adversarial Robustness
Ziang Yan, Yiwen Guo, Changshui Zhang
2018-12-21
Computer Vision and Pattern Recognition · Computer Science
Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection
Hui Liu, Bo Zhao, Yuefeng Peng, Weidong Li +1
2022-05-27
Artificial Intelligence · Computer Science
"That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks
Edoardo Mosca, Shreyash Agarwal, Javier Rando, Georg Groh
2023-06-30
Machine Learning · Computer Science
Detecting and Correcting Adversarial Images Using Image Processing Operations
Huy H. Nguyen, Minoru Kuribayashi, Junichi Yamagishi, Isao Echizen
2020-01-01
Computer Vision and Pattern Recognition · Computer Science
Improving Transferability of Adversarial Examples with Input Diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai +3
2019-06-04
Machine Learning · Computer Science
Adversarial Reprogramming of Neural Networks
Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein
2019-09-12
R2 v1 2026-06-23T03:25:40.777Z