Papers with Code 아카이브

Adversarial Defense

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

10

보존된 평가 기록

28

코드

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

https://github.com/khieu/cifar10_challenge

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/lemonadec/Relevance-between-Accuracy-under-Black-box-Attack-and-the-Similarity-between-Networks

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/lirundong/quant-pack

Feature Denoising for Improving Adversarial Robustness

https://github.com/llylly/dsrs

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/locuslab/convex_adversarial

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/locuslab/fast_adversarial

On Evaluating Adversarial Robustness

https://github.com/locuslab/robust_overfitting

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/loes5307/vocaladversary2022

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/luizgh/adversarial_signatures

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/matanbt/attack-tabular

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/microsoft/distance-learner

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/mnswdhw/DefenseGAN-and-Cowboy-Defense

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

https://github.com/mnswdhw/InvGAN-Pytorch

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

https://github.com/mr-eggplant/eata

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/mr-eggplant/sar

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/mwojnars/nifty

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/ndb796/pytorch-adversarial-training-cifar

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/neurai-lab/dsp

Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

https://github.com/nonparametric-adversarial/nonparametric

Robust Decision Trees Against Adversarial Examples

https://github.com/nutellamok/advrush

Theoretically Principled Trade-off between Robustness and Accuracy

https://github.com/nybupt/athena

ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense

https://github.com/openai/cleverhans

The Limitations of Deep Learning in Adversarial Settings

https://github.com/optimization-for-data-driven-science/dair

Theoretically Principled Trade-off between Robustness and Accuracy

https://github.com/peck94/cann-detector

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/pralab/secml

Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

https://github.com/princeton-computational-imaging/allyouneedisraw

All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines

https://github.com/revbucket/mister_ed

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/rtoth11/NNAdversary-MIPVerify

Certified Defenses against Adversarial Examples

https://github.com/salomonhotegni/MOREL

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/sayakpaul/Denoised-Smoothing-TF

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/scenarri/s2m-tea

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/shashankskagnihotri/adv-corrected-ddcat-cospgd

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/shijiel2/cleverhans

The Limitations of Deep Learning in Adversarial Settings

https://github.com/simon0987/Fast_FGSM

On Evaluating Adversarial Robustness

https://github.com/softsys4ai/FlexiBO

ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial Defense

https://github.com/tcwangshiqi-columbia/Neurify

Efficient Formal Safety Analysis of Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.