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Adversarial Defense

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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보존된 평가 기록

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코드

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

https://github.com/youzunzhi/nim-advdef

공식 코드

Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense

https://github.com/ysharma1126/caad_18

공식 코드

CAAD 2018: Generating Transferable Adversarial Examples

https://github.com/yueeeeeeee/recsys-substitution-defense

공식 코드

Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders

https://github.com/ziquanliu/ICML2024-AT-UR

공식 코드

The Pitfalls and Promise of Conformal Inference Under Adversarial Attacks

https://github.com/zjwei-nuist/SRAE

공식 코드

Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks

https://github.com/zzd1992/Adversarial-Defense-by-Suppressing-High-Frequencies

공식 코드

Adversarial Defense by Suppressing High-frequency Components

https://github.com/zzzhangboya/scoreopt

공식 코드

Enhancing Adversarial Robustness via Score-Based Optimization

https://worksheets.codalab.org/worksheets/0xa21e794020bb474d8804ec7bc0543f52

공식 코드

Certified Defenses against Adversarial Examples

https://github.com/2023-MindSpore-1/ms-code-222/tree/main/resnet

ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies

https://github.com/2024-MindSpore-1/Code4/tree/main/ResNet

ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies

https://github.com/AI-secure/Transferability-Reduced-Smooth-Ensemble

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/Ahmadreza-Jeddi/Learn2Perturb

Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness

https://github.com/AlanChou/Adversarial-Training-for-Free

Adversarial Training for Free!

https://github.com/AngusG/cleverhans-attacking-bnns

The Limitations of Deep Learning in Adversarial Settings

https://github.com/Annonymous-repos/attacks-in-pytorch

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

https://github.com/BaoWangMath/Graph-Structured-Recurrent-Neural-Nets-

ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies

https://github.com/Cadden/paddle-adver

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/Cody2333/WassNet

advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch

https://github.com/EPFL-VILAB/XDEnsembles

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/ElisaNguyen/dl-defense-pgd

advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch

https://github.com/FPNAS/LBGAT

Decoupled Kullback-Leibler Divergence Loss

https://github.com/Faceplugin-ltd/FaceLivenessDetection-Android

AdvFAS: A robust face anti-spoofing framework against adversarial examples

https://github.com/GraphReshape/GraphReshape

Adversarial Examples on Graph Data: Deep Insights into Attack and Defense

https://github.com/Hadisalman/robust-verify-benchmark

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/HowToMakeABomb101/Hot2MakeAB0mbSite

The Limitations of Deep Learning in Adversarial Settings

https://github.com/IBM/Autozoom-Attack

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

https://github.com/IBM/ZOO-Attack

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

https://github.com/Jeffkang-94/pytorch-adversarial-attack

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/KatyaKom/Python-Z3-NNVerif

Safety Verification of Deep Neural Networks

https://github.com/KnowledgeDiscovery/FaceSec

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/Koukyosyumei/NAIST-FedML-Experiments

A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning

https://github.com/LinaMaudlej/Weighted-Smoothing

Smoothed Inference for Adversarially-Trained Models

https://github.com/Line290/FeatureAttack

Improving Adversarial Robustness via Guided Complement Entropy

https://github.com/MKYucel/zero_shot_corruption_benchmarks

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/P2333/Max-Mahalanobis-Training

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/RSMung/DefenseGAN-pytorch

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

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