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Inference Attack

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/iamgroot42/dissecting_dist_inf

공식 코드

Dissecting Distribution Inference

https://github.com/iamgroot42/dissecting_distribution_inference

공식 코드

Dissecting Distribution Inference

https://github.com/iamgroot42/form_est_dist_risks

공식 코드

Formalizing and Estimating Distribution Inference Risks

https://github.com/iamgroot42/formestdistrisks

공식 코드

Formalizing and Estimating Distribution Inference Risks

https://github.com/iamgroot42/iha_hild

공식 코드

Do Parameters Reveal More than Loss for Membership Inference?

https://github.com/inspire-group/membership-inference-evaluation

공식 코드

Systematic Evaluation of Privacy Risks of Machine Learning Models

https://github.com/inspire-group/privacy-vs-robustness

공식 코드

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

https://github.com/iyempissy/rebMIGraph

공식 코드

Membership Inference Attack on Graph Neural Networks

https://github.com/izakariyya/idg-dp

공식 코드

Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition

https://github.com/jayoungkim408/mia

공식 코드

An Empirical Study on the Membership Inference Attack against Tabular Data Synthesis Models

https://github.com/jiangnanh/ppml-workshop

공식 코드

LTU Attacker for Membership Inference

https://github.com/jiepku/mia-safecompress

공식 코드

Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment

https://github.com/jiepku/safecompress

공식 코드

Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment

https://github.com/jinzhuoran/rwku

공식 코드

RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models

https://github.com/jlim13/keystroke-inference-attack-synthetic-dataset-generator-

공식 코드

Revisiting the Threat Space for Vision-based Keystroke Inference Attacks

https://github.com/johnmath/snap-sp23

공식 코드

SNAP: Efficient Extraction of Private Properties with Poisoning

https://github.com/jorghyq2016/fedhssl

공식 코드

A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning

https://github.com/khanhnguyen21006/mia_docvqa

공식 코드

DocMIA: Document-Level Membership Inference Attacks against DocVQA Models

https://github.com/kong13661/pia

공식 코드

An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization

https://github.com/leilynourbakhsh/inf2guard

공식 코드

Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks

https://github.com/liar-mask/fedmia

공식 코드

FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning

https://github.com/lions-epfl/vl-mia

공식 코드

Membership Inference Attacks against Large Vision-Language Models

https://github.com/lioutang/unlearn-apollo-attack

공식 코드

Apollo: A Posteriori Label-Only Membership Inference Attack Towards Machine Unlearning

https://github.com/liuyugeng/ml-doctor

공식 코드

ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models

https://github.com/lukasstruppek/class_attribute_inference_attacks

공식 코드

Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations

https://github.com/machine-learning-security-lab/mia_prune

공식 코드

Membership Inference Attacks and Defenses in Neural Network Pruning

https://github.com/meghdad92/scrub

공식 코드

Towards Unbounded Machine Unlearning

https://github.com/mgcsls/mechanisms-global-properties

공식 코드

Protecting Global Properties of Datasets with Distribution Privacy Mechanisms

https://github.com/microsoft/intrinsic-private-sgd

공식 코드

An Empirical Study on the Intrinsic Privacy of SGD

https://github.com/mingxue-xu/ori

공식 코드

Data Origin Inference in Machine Learning

https://github.com/minxingzhang/miagm

공식 코드

Generated Distributions Are All You Need for Membership Inference Attacks Against Generative Models

https://github.com/multimodalmi/multimodal-membership-inference

공식 코드

M^4I: Multi-modal Models Membership Inference

https://github.com/negedng/ma-fl-mia

공식 코드

Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning

https://github.com/nehemya/Algo-Trade-Adversarial-Examples

공식 코드

Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders

https://github.com/ocram17/duck

공식 코드

DUCK: Distance-based Unlearning via Centroid Kinematics

https://github.com/omarerak/clad

공식 코드

Contrastive Learning and Adversarial Disentanglement for Task-Oriented Semantic Communications

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