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FILE: LICENSE
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MIT License

Copyright (c) 2021 ZhengyuZhao

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


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FILE: README.md
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# A curated list of AI Security & Privacy academic events

<!-- Beyond other resources (e.g. [papers](https://nicholas.carlini.com/writing/2019/all-adversarial-example-papers.html) and [tookits](https://opensourcelibs.com/libs/adversarial-examples)), here we provide a curated list of related events (e.g. workshops and tutorials) and hope it can help light up your journey on AI Security and Privacy. :smile_cat:	 -->

## Seminar
  + [**NLP & LLM Security**](https://sig.llmsecurity.net/talks/)
  + [**Privacy and Security in ML (PriSec-ML)**](https://prisec-ml.github.io/)
  + [**Machine Learning Security (MLSec)**](https://www.youtube.com/c/MLSec/playlists)
  + [**Seminars on Security & Privacy in Machine Learning (ML S&P)**](https://vsehwag.github.io/SPML_seminar/)  
  + [**AI Security and Privacy (AISP)**](https://space.bilibili.com/1556922191/?spm_id_from=333.999.0.0) (in Chinese)


## Conference
  + [**IEEE Conference on Secure and Trustworthy Machine Learning (2022-)**](https://satml.org/)
  + [**The Conference on Applied Machine Learning in Information Security (2017-)**](https://www.camlis.org/)

## Workshop

- ### Security & Privacy
  + **Artificial Intelligence and Security** ([CCS 2008-](https://aisec.cc/))
  + **Deep Learning Security and Privacy** ([S&P 2018-](https://dlsp2024.ieee-security.org/))
  + **Dependable and Secure Machine Learning** ([DSN 2018-](https://dependablesecureml.github.io/index.html))
  + **Security Architectures for Generative-AI Systems** ([S&P 2024](https://sites.google.com/view/sagai2024/home))
  + **AI System with Confidential Computing** ([NDSS 2024](https://sites.google.com/view/aiscc2024))

- ### Machine Learning & Artificial Intelligence
  + **Red Teaming GenAI: What Can We Learn from Adversaries?** ([NeurIPS 2024](https://redteaming-gen-ai.github.io/))
  + **Safe Generative AI** ([NeurIPS 2024](https://safegenaiworkshop.github.io/))
  + **Towards Safe & Trustworthy Agents** ([NeurIPS 2024](https://www.mlsafety.org/events/neurips/2024))
  + **Socially Responsible Language Modelling Research** ([NeurIPS 2024](https://solar-neurips.github.io/))
  + **Next Generation of AI Safety** ([ICML 2024](https://icml-nextgenaisafety.github.io/))
  + **Trustworthy Multi-modal Foundation Models and AI Agents** ([ICML 2024](https://icml-tifa.github.io/))
  + **Secure and Trustworthy Large Language Models** ([ICLR 2024](https://set-llm.github.io/))
  + **Reliable and Responsible Foundation Models** ([ICLR 2024](https://iclr-r2fm.github.io/))
  + **Privacy Regulation and Protection in Machine Learning** ([ICLR 2024](https://pml-workshop.github.io/iclr24/))
  + **Responsible Language Models** ([AAAI 2024](https://sites.google.com/vectorinstitute.ai/relm2024/home))
  + **Privacy-Preserving Artificial Intelligence** ([AAAI 2020-2024](https://aaai-ppai24.github.io/))
  + **Practical Deep Learning in the Wild** ([CAI 2024, AAAI 2022-2023](https://practical-dl.github.io/))
  + **Backdoors in Deep Learning: The Good, the Bad, and the Ugly** ([NeurIPS 2023](https://neurips2023-bugs.github.io/))
  + **Trustworthy and Reliable Large-Scale Machine Learning Models** ([ICLR 2023](https://rtml-iclr2023.github.io/))
  + **Backdoor Attacks and Defenses in Machine Learning** ([ICLR 2023](https://iclr23-bands.github.io/))
  + **Privacy, Accountability, Interpretability, Robustness, Reasoning on Structured Data** ([ICLR 2022](https://pair2struct-workshop.github.io/))
  + **Security and Safety in Machine Learning Systems** ([ICLR 2021](https://aisecure-workshop.github.io/aml-iclr2021/))
  + **Robust and Reliable Machine Learning in the Real World** ([ICLR 2021](https://sites.google.com/connect.hku.hk/robustml-2021/home))
  + **Towards Trustworthy ML: Rethinking Security and Privacy for ML** ([ICLR 2020](https://trustworthyiclr20.github.io/))
  + **Safe Machine Learning: Specification, Robustness and Assurance** ([ICLR 2019](https://sites.google.com/view/safeml-iclr2019))
  + **New Frontiers in Adversarial Machine Learning** ([ICML 2022-2023](https://advml-frontier.github.io/)) 
  + **Theory and Practice of Differential Privacy** ([ICML 2021-2022](https://tpdp.journalprivacyconfidentiality.org/2022/)) 
  + **Uncertainty & Robustness in Deep Learning** ([ICML 2020-2021](https://sites.google.com/view/udlworkshop2021/home))
  + **A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning** ([ICML 2021](https://advml-workshop.github.io/icml2021/))
  + **Security and Privacy of Machine Learning** ([ICML 2019](https://icml2019workshop.github.io/)) 
  + **Socially Responsible Machine Learning** ([NeurIPS 2022](https://tsrml2022.github.io/), [ICLR 2022](https://iclrsrml.github.io/), [ICML 2021](https://icmlsrml2021.github.io/))
  + **ML Safety** ([NeurIPS 2022](https://neurips2022.mlsafety.org/))
  + **Privacy in Machine Learning** ([NeurIPS 2021](https://priml2021.github.io/))
  + **Dataset Curation and Security** ([NeurIPS 2020](http://securedata.lol/))
  + **Security in Machine Learning** ([NeurIPS 2018](https://secml2018.github.io/))
  + **Machine Learning and Computer Security** ([NeurIPS 2017](https://machine-learning-and-security.github.io/))
  + **Adversarial Training** ([NeurIPS 2016](https://sites.google.com/site/nips2016adversarial/))
  + **Reliable Machine Learning in the Wild** ([NeurIPS 2016](https://sites.google.com/site/wildml2016nips/home))
  + **Adversarial Learning Methods for Machine Learning and Data Mining** ([KDD 2019-2022](https://sites.google.com/view/advml))
  + **Privacy Preserving Machine Learning** ([FOCS 2022, CCS 2021, NeurIPS 2020, CCS 2019, NeurIPS 2018](https://ppml-workshop.github.io/))
  + **SafeAI** ([AAAI 2019-2022](https://safeai.webs.upv.es/))
  + **Adversarial Machine Learning and Beyond** ([AAAI 2022](https://advml-workshop.github.io/aaai2022/))
  + **Towards Robust, Secure and Efficient Machine Learning** ([AAAI2021](http://federated-learning.org/rseml2021/))
  + **AISafety** ([IJCAI 2019-2022](https://www.aisafetyw.org/))

- ### Computer Vision
  + **The Dark Side of Generative AIs and Beyond** ([ECCV 2024](https://privacy-preserving-computer-vision.github.io/eccv24.html))
  + **Trust What You learN** ([ECCV 2024](https://twyn.unimore.it/))
  + **Privacy for Vision & Imaging** ([ECCV 2024](https://sites.google.com/view/rcv-at-eccv-2022/home?authuser=0))
  + **Adversarial Machine Learning on Computer Vision** ([CVPR 2024](https://cvpr24-advml.github.io/), [CVPR 2023](https://robustart.github.io/), [CVPR 2022](https://artofrobust.github.io/), [CVPR 2020](https://adv-workshop-2020.github.io/))
  + **Secure and Safe Autonomous Driving** ([CVPR 2023](https://ai-secure.github.io/SSAD2023/))
  + **Adversarial Robustness in the Real World** ([ICCV 2023](https://iccv23-arow.github.io/), [ECCV 2022](https://eccv22-arow.github.io/), [ICCV 2021](https://iccv21-adv-workshop.github.io/), [CVPR 2021](https://aisecure-workshop.github.io/amlcvpr2021/), [ECCV 2020](https://eccv20-adv-workshop.github.io/), [CVPR 2020](https://adv-workshop-2020.github.io/), [CVPR 2019](https://amlcvpr2019.github.io/))
  + **The Bright and Dark Sides of Computer Vision: Challenges and Opportunities for Privacy and Security** ([CVPR 2021](https://quovadiscvpr.cispa.de/), [ECCV 2020](https://cvcops20.cispa.saarland/), [CVPR 2019](https://cvcops19.cispa.saarland/), [CVPR 2018](https://vision.soic.indiana.edu/bright-and-dark-workshop-2018/), [CVPR 2017](https://vision.soic.indiana.edu/bright-and-dark-workshop-2017/))
  + **Responsible Computer Vision** ([ECCV 2022](https://sites.google.com/view/rcv-at-eccv-2022/home?authuser=0))
  + **Safe Artificial Intelligence for Automated Driving** ([ECCV 2022](https://sites.google.com/view/saiad2022))
  + **Adversarial Learning for Multimedia** ([ACMMM 2021](https://advm-workshop-2021.github.io/))
  + **Adversarial Machine Learning towards Advanced Vision Systems** ([ACCV 2022](https://sites.google.com/view/workshop-of-amlavs))

- ### Natural Language Processing
  + **Trustworthy Natural Language Processing** ([2021-2024](https://trustnlpworkshop.github.io/))
  + **Privacy in Natural Language Processing** ([ACL 2024](https://sites.google.com/view/privatenlp/), [NAACL 2022](https://sites.google.com/view/privatenlp/home/naacl-2022), [NAACL 2021](https://sites.google.com/view/privatenlp/home/naacl-2021), [EMNLP 2020](https://sites.google.com/view/privatenlp/home/emnlp-2020), [WSDM 2020](https://sites.google.com/view/privatenlp/home/wsdm-2020))
  + **BlackboxNLP** ([2018-2024](https://blackboxnlp.github.io/))
  
- ### Information Retrieval
  + **Online Misinformation- and Harm-Aware Recommender Systems** ([RecSys 2021](https://ohars-recsys.isistan.unicen.edu.ar/topics-of-interest), [RecSys 2020](https://ohars-recsys2020.isistan.unicen.edu.ar/))
  + **Adversarial Machine Learning for Recommendation and Search** ([CIKM 2021](https://sisinflab.github.io/adverse2021/))

## Tutorial  
- ### Machine Learning & Artificial Intelligence
  + **Quantitative Reasoning About Data Privacy in Machine Learning** ([ICML 2022](https://icml.cc/Conferences/2022/Schedule?showEvent=18439))
  + **Foundational Robustness of Foundation Models** ([NeurIPS 2022](https://sites.google.com/view/neurips2022-frfm-turotial/home))
  + **Adversarial Robustness - Theory and Practice** ([NeurIPS 2018](https://adversarial-ml-tutorial.org/))
  + **Towards Adversarial Learning: from Evasion Attacks to Poisoning Attacks** ([KDD 2022](https://sites.google.com/view/kdd22-tutorial-adv-learn/))
  + **Adversarial Robustness in Deep Learning: From Practices to Theories** ([KDD 2021](https://sites.google.com/view/kdd21-tutorial-adv-robust/))
  + **Adversarial Attacks and Defenses: Frontiers, Advances and Practice** ([KDD 2020](https://sites.google.com/view/kdd-2020-attack-and-defense/home))
  + **Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications** ([ICDM 2020](https://tutorial.trustdeeplearning.com/))
  + **Adversarial Machine Learning for Good** ([AAAI 2022](https://sites.google.com/view/advml4good))
  + **Adversarial Machine Learning** ([AAAI 2018](https://aaai18adversarial.github.io/index.html#syl))

- ### Computer Vision
  + **Adversarial Machine Learning in Computer Vision** ([CVPR 2021](https://advmlincv.github.io/cvpr21-tutorial/))
  + **Practical Adversarial Robustness in Deep Learning: Problems and Solutions** ([CVPR 2021](https://sites.google.com/view/par-2021))
  + **Adversarial Robustness of Deep Learning Models** ([ECCV 2020](https://sites.google.com/umich.edu/eccv-2020-adv-robustness))
  + **Deep Learning for Privacy in Multimedia** ([ACMMM 2020](http://cis.eecs.qmul.ac.uk/privacymultimedia.html))
 
- ### Natural Language Processing
  + **Vulnerabilities of Large Language Models to Adversarial Attacks** ([ACL 2024](https://llm-vulnerability.github.io/))
  + **Robustness and Adversarial Examples in Natural Language Processing** ([EMNLP 2021](https://2021.emnlp.org/tutorials))
  + **Deep Adversarial Learning for NLP** ([NAACL 2019](https://sites.cs.ucsb.edu/~william/papers/AdvNLP-NAACL2019.pdf))

- ### Information Retrieval
  + **Adversarial Machine Learning in Recommender Systems** ([ECIR 2021](https://www.youtube.com/watch?v=8V4TLdYMit8&list=PLted5MzCy6KwnlE3kFmeQJhDJCbS1lAt0&index=3), [RecSys 2020](https://www.youtube.com/watch?v=tjzykHbBd0w&list=PLted5MzCy6KwnlE3kFmeQJhDJCbS1lAt0&index=2), [WSDM 2020](https://github.com/sisinflab/amlrecsys-tutorial/blob/master/Tutorial-AML-RecSys-WSDM2020.pdf))

## Special Session
  + **Special Track on Safe and Robust AI** ([AAAI 2023](https://aaai.org/Conferences/AAAI-23/safeandrobustai/))
  + **Special Session on Adversarial Learning for Multimedia Understanding and Retrieval** ([ICMR 2022](https://al4mur.github.io/))
  + **Special Session on Adversarial Attack and Defense** ([APSIPA 2022](https://sites.google.com/ahduni.edu.in/2022-apsipa-ss-aad))
  + **Special Session on Information Security meets Adversarial Examples** ([WIFS 2019](https://signalprocessingsociety.org/WIFS2019/index9f1c.html?q=node/18))