From c941beef8a319e77205eac7260bb9439c7f453d2 Mon Sep 17 00:00:00 2001 From: AlvinYu <125203137+AlvinYu025@users.noreply.github.com> Date: Sun, 25 Jun 2023 20:55:01 +0800 Subject: [PATCH] Update README.md --- README.md | 61 ++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 60 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 7b15961..a53a835 100644 --- a/README.md +++ b/README.md @@ -93,7 +93,7 @@ Philosophical Transactions of the Royal Society A 2018. Algorithms that remember | 2022 | SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination | ECCV | white-box | [Paper](https://arxiv.org/pdf/2207.12263.pdf) | | | 2022 | UnSplit: Data-Oblivious Model Inversion, Model Stealing, andLabel Inference Attacks Against Split Learning | WPES | S | [Paper](https://arxiv.org/pdf/2108.09033.pdf) | [code](https://github.com/ege-erdogan/unsplit) | | 2022 | MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity | NDSS | white-box | [Paper](https://www.cs.purdue.edu/homes/an93/static/papers/ndss2022_model_inversion.pdf) | [code](https://github.com/njuaplusplus/mirror) | -| 2021 | Practical Black Box Model Inversion Attacks Against Neural Nets | ECML PKDD | black-box | [Paper]() | | +| 2021 | Practical Black Box Model Inversion Attacks Against Neural Nets | ECML PKDD | black-box | [Paper](https://link.springer.com/content/pdf/10.1007/978-3-030-93733-1.pdf?pdf=button) | | | 2021 | Model Inversion Attack against a Face Recognition System in a Black-Box Setting | APSIPA | black-box | [Paper](http://www.apsipa.org/proceedings/2021/pdfs/0001800.pdf) | | | 2020 | Generative model-inversion attacks against deep neural networks | CVPR | white-box | [Paper](https://arxiv.org/pdf/1911.07135.pdf) | [code](https://github.com/AI-secure/GMI-Attack) | | 2020 | Privacy Preserving Facial Recognition Against Model Inversion Attacks | IEEE | white-box | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9322508) | | @@ -269,6 +269,65 @@ Arxiv 2022 - Defending against Reconstruction Attacks through Differentially Pri IEEE 2021 - Defending Against Model Inversion Attack by Adversarial Examples. [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9527945&tag=1) +IEEE 2023 - Sparse Black-Box Inversion Attack with Limited Information +[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10095514) +[[code]](https://github.com/Tencent/TFace/tree/master/recognition) + +Arxiv 2023 - Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack +[[paper]](https://arxiv.org/pdf/2304.11436.pdf) +[[code]](https://github.com/FLAIR-THU/PairedLogitsInversion) + +AAAI 2023 - Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network +[[paper]](https://arxiv.org/pdf/2302.09814.pdf) +[[code]](https://github.com/lethesec/plg-mi-attack) + +IEEE 2023 - C2FMI: Corse-to-Fine Black-box Model Inversion Attack +[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10148574) + +IEEE 2023 - Boosting Model Inversion Attacks with Adversarial Examples +[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10148576) + +CVPR 2023 - Reinforcement Learning-Based Black-Box Model Inversion Attacks +[[paper]](https://arxiv.org/pdf/2304.04625.pdf) +[[code]](https://github.com/HanGyojin/RLB-MI) + +CVPR 2023 - Re-thinking Model Inversion Attacks Against Deep Neural Networks +[[paper]](https://arxiv.org/pdf/2304.01669.pdf) +[[code]](https://github.com/sutd-visual-computing-group/Re-thinking_MI) | + +IEEE 2022 - One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy +[[paper]](https://arxiv.org/pdf/2203.06580.pdf) + +WACV 2022 - Reconstructing Training Data from Diverse ML Models by Ensemble Inversion +[[paper]](https://arxiv.org/pdf/2111.03702.pdf) + +ECCV 2022 - SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination +[[paper]](https://arxiv.org/pdf/2207.12263.pdf) + +WPES 2022- UnSplit: Data-Oblivious Model Inversion, Model Stealing, andLabel Inference Attacks Against Split Learning +[[paper]](https://arxiv.org/pdf/2108.09033.pdf) +[[code]](https://github.com/ege-erdogan/unsplit) + +NDSS 2022 - MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity +[[paper]](https://www.cs.purdue.edu/homes/an93/static/papers/ndss2022_model_inversion.pdf) +[[code]](https://github.com/njuaplusplus/mirror) | + +ECML PKDD 2021 - Practical Black Box Model Inversion Attacks Against Neural Nets +[[paper]](https://link.springer.com/content/pdf/10.1007/978-3-030-93733-1.pdf?pdf=button) + +APSIPA 2021 - Model Inversion Attack against a Face Recognition System in a Black-Box Setting +[[paper]](http://www.apsipa.org/proceedings/2021/pdfs/0001800.pdf) + +CVPR 2020 - Generative model-inversion attacks against deep neural networks +[[paper]](https://arxiv.org/pdf/1911.07135.pdf) +[[code]](https://github.com/AI-secure/GMI-Attack) + +IEEE 2020 - Privacy Preserving Facial Recognition Against Model Inversion Attacks +[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9322508) + +IEEE 2020 - Broadening Differential Privacy for Deep LearningAgainst Model Inversion Attacks +[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9378274) + ## Graph learning domain | Year | Title | Adversarial Knowledge | Venue | Paper Link | Code Link | | ---- | ----- | -------------------- | ----- | ---------- | --------- |