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Adversarial Defense

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

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보존된 평가 기록

28

코드

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

https://github.com/bingcheng45/hnr-extension

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/blaisedelattre/bridging_the_gap_rs

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/boyellow/adaad

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/cdluminate/advrank

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/cdluminate/advrank-pub

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/cihangxie/DI-2-FGSM

Mitigating Adversarial Effects Through Randomization

https://github.com/cleverhans-lab/cleverhans

The Limitations of Deep Learning in Adversarial Settings

https://github.com/coallaoh/whitenblackbox

Delving into Transferable Adversarial Examples and Black-box Attacks

https://github.com/cs-giung/course-dl-TP

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/dacostaHugo/Adversarial_attacks

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/deeplearning-wisc/hypo

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/edwardjhu/improved_wasserstein

Wasserstein Adversarial Examples via Projected Sinkhorn Iterations

https://github.com/ee17b031-iittp/Projected-Gradient-Descent-with-CIFAR10

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/eldadp100/Towards-Deep-Learning-Models-Resistant-to-Adversarial-Attacks-Implementation

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/eldadp100/cnn_course_final

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/elites2k19/prism-attack

The Limitations of Deep Learning in Adversarial Settings

https://github.com/facebookresearch/augmentation-corruption

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/feedbackward/addro

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

https://github.com/findoctor/YOPO_reproduce

You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle

https://github.com/formal-verification-research/NJSMA

The Limitations of Deep Learning in Adversarial Settings

https://github.com/gatheluck/Stronghold

Testing Robustness Against Unforeseen Adversaries

https://github.com/gillesvntnu/gcn_unet_agreement_demo

GraphDefense: Towards Robust Graph Convolutional Networks

https://github.com/gitbosun/advdefense_csc

Adversarial Defense by Stratified Convolutional Sparse Coding

https://github.com/goldblum/AdversariallyRobustDistillation

Theoretically Principled Trade-off between Robustness and Accuracy

https://github.com/henry8527/GCE

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/hope-yao/robust_attention_cifar

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/hrdwsong/TDLMR2AA-Paddle

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/huanranchen/AdversarialAttacks/tree/main/experiments/DiffusionClassifier

Robust Classification via a Single Diffusion Model

https://github.com/huisun317/snn

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

https://github.com/iVMCL/AOGNet-v2

AOGNets: Compositional Grammatical Architectures for Deep Learning

https://github.com/iirishikaii/cleverhans

The Limitations of Deep Learning in Adversarial Settings

https://github.com/imrahulr/adversarial_robustness_pytorch

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/ivMCL/AttentiveNorm_Detection

AOGNets: Compositional Grammatical Architectures for Deep Learning

https://github.com/jayjaynandy/RBF-CNN

Certified Adversarial Robustness via Randomized Smoothing

https://github.com/johnsonkee/graduate_design

The Limitations of Deep Learning in Adversarial Settings

https://github.com/kay-ck/bsc-attack

PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning

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