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A curated list of academic events on AI Security & Privacy
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A curated list of AI Security & Privacy academic events
Seminar
NLP & LLM Security
Privacy and Security in ML (PriSec-ML)
Machine Learning Security (MLSec)
Seminars on Security & Privacy in Machine Learning (ML S&P)
AI Security and Privacy (AISP)
(in Chinese)
Conference
IEEE Conference on Secure and Trustworthy Machine Learning (2022-)
The Conference on Applied Machine Learning in Information Security (2017-)
Workshop
Security & Privacy
Artificial Intelligence and Security
(
CCS 2008-
)
Deep Learning Security and Privacy
(
S&P 2018-
)
Dependable and Secure Machine Learning
(
DSN 2018-
)
Security Architectures for Generative-AI Systems
(
S&P 2024
)
AI System with Confidential Computing
(
NDSS 2024
)
Machine Learning & Artificial Intelligence
Secure and Trustworthy Large Language Models
(
ICLR 2024
)
Reliable and Responsible Foundation Models
(
ICLR 2024
)
Privacy Regulation and Protection in Machine Learning
(
ICLR 2024
)
Responsible Language Models
(
AAAI 2024
)
Privacy-Preserving Artificial Intelligence
(
AAAI 2020-2024
)
Practical Deep Learning in the Wild
(
CAI 2024, AAAI 2022-2023
)
Backdoors in Deep Learning: The Good, the Bad, and the Ugly
(
NeurIPS 2023
)
Trustworthy and Reliable Large-Scale Machine Learning Models
(
ICLR 2023
)
Backdoor Attacks and Defenses in Machine Learning
(
ICLR 2023
)
Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data
(
ICLR 2022
)
Security and Safety in Machine Learning Systems
(
ICLR 2021
)
Robust and Reliable Machine Learning in the Real World
(
ICLR 2021
)
Towards Trustworthy ML: Rethinking Security and Privacy for ML
(
ICLR 2020
)
Safe Machine Learning: Specification, Robustness and Assurance
(
ICLR 2019
)
New Frontiers in Adversarial Machine Learning
(
ICML 2022-2023
)
Theory and Practice of Differential Privacy
(
ICML 2021-2022
)
Uncertainty & Robustness in Deep Learning
(
ICML 2020-2021
)
A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning
(
ICML 2021
)
Security and Privacy of Machine Learning
(
ICML 2019
)
Socially Responsible Machine Learning
(
NeurIPS 2022
,
ICLR 2022
,
ICML 2021
)
ML Safety
(
NeurIPS 2022
)
Privacy in Machine Learning
(
NeurIPS 2021
)
Dataset Curation and Security
(
NeurIPS 2020
)
Security in Machine Learning
(
NeurIPS 2018
)
Machine Learning and Computer Security
(
NeurIPS 2017
)
Adversarial Training
(
NeurIPS 2016
)
Reliable Machine Learning in the Wild
(
NeurIPS 2016
)
Adversarial Learning Methods for Machine Learning and Data Mining
(
KDD 2019-2022
)
Privacy Preserving Machine Learning
(
FOCS 2022, CCS 2021, NeurIPS 2020, CCS 2019, NeurIPS 2018
)
SafeAI
(
AAAI 2019-2022
)
Adversarial Machine Learning and Beyond
(
AAAI 2022
)
Towards Robust, Secure and Efficient Machine Learning
(
AAAI2021
)
AISafety
(
IJCAI 2019-2022
)
Computer Vision
Adversarial Machine Learning on Computer Vision
(
CVPR 2024
,
CVPR 2023
,
CVPR 2022
,
CVPR 2020
)
Secure and Safe Autonomous Driving
(
CVPR 2023
)
Adversarial Robustness in the Real World
(
ICCV 2023
,
ECCV 2022
,
ICCV 2021
,
CVPR 2021
,
ECCV 2020
,
CVPR 2020
,
CVPR 2019
)
The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security
(
CVPR 2021
,
ECCV 2020
,
CVPR 2019
,
CVPR 2018
,
CVPR 2017
)
Responsible Computer Vision
(
ECCV 2022
)
Safe Artificial Intelligence for Automated Driving
(
ECCV 2022
)
Adversarial Learning for Multimedia
(
ACMMM 2021
)
Adversarial Machine Learning towards Advanced Vision Systems
(
ACCV 2022
)
Natural Language Processing
BlackboxNLP
(
EMNLP 2022
,
EMNLP 2021
,
EMNLP 2020
,
ACL 2019
,
EMNLP 2018
)
Information Retrieval
Online Misinformation- and Harm-Aware Recommender Systems
(
RecSys 2021
,
RecSys 2020
)
Adversarial Machine Learning for Recommendation and Search
(
CIKM 2021
)
Tutorial
Machine Learning & Artificial Intelligence
Quantitative Reasoning About Data Privacy in Machine Learning
(
ICML 2022
)
Foundational Robustness of Foundation Models
(
NeurIPS 2022
)
Adversarial Robustness - Theory and Practice
(
NeurIPS 2018
)
Towards Adversarial Learning: from Evasion Attacks to Poisoning Attacks
(
KDD 2022
)
Adversarial Robustness in Deep Learning: From Practices to Theories
(
KDD 2021
)
Adversarial Attacks and Defenses: Frontiers, Advances and Practice
(
KDD 2020
)
Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications
(
ICDM 2020
)
Adversarial Machine Learning for Good
(
AAAI 2022
)
Adversarial Machine Learning
(
AAAI 2018
)
Computer Vision
Adversarial Machine Learning in Computer Vision
(
CVPR 2021
)
Practical Adversarial Robustness in Deep Learning: Problems and Solutions
(
CVPR 2021
)
Adversarial Robustness of Deep Learning Models
(
ECCV 2020
)
Deep Learning for Privacy in Multimedia
(
ACMMM 2020
)
Natural Language Processing
Vulnerabilities of Large Language Models to Adversarial Attacks
(
ACL 2024
)
Robustness and Adversarial Examples in Natural Language Processing
(
EMNLP 2021
)
Deep Adversarial Learning for NLP
(
NAACL 2019
)
Information Retrieval
Adversarial Machine Learning in Recommender Systems
(
ECIR 2021
,
RecSys 2020
,
WSDM 2020
)
Special Session
Special Track on Safe and Robust AI
(
AAAI 2023
)
Special Session on Adversarial Learning for Multimedia Understanding and Retrieval
(
ICMR 2022
)
Special Session on Adversarial Attack and Defense
(
APSIPA 2022
)
Special Session on Information Security meets Adversarial Examples
(
WIFS 2019
)
Open Source Agenda is not affiliated with "AI Security And Privacy Events" Project. README Source:
ZhengyuZhao/AI-Security-and-Privacy-Events
Stars
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Open Issues
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Last Commit
3 months ago
Repository
ZhengyuZhao/AI-Security-and-Privacy-Events
License
MIT
Tags
Adversarial Examples
Adversarial Machine Learning
Ai Privacy
Ai Security
Data Poisoning
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