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Adversarial Robustness

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평가표: 7
보존된 평가 기록: 33

Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.

코드

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

https://github.com/bethgelab/foolbox/blob/master/foolbox/attacks/ead.py

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

https://github.com/bethgelab/foolbox/blob/master/foolbox/attacks/gen_attack.py

GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

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

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/bogdan-kulynych/textfool

Evading classifiers in discrete domains with provable optimality guarantees

https://github.com/boyellow/adaad

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/carl-ch/gambit

Architectural Resilience to Foreground-and-Background Adversarial Noise

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/cemanil/LNets

Sorting out Lipschitz function approximation

https://github.com/chenpf1025/IDN/blob/master/ops.py

A Closer Look at Memorization in Deep Networks

https://github.com/christopherbrix/debona

Debona: Decoupled Boundary Network Analysis for Tighter Bounds and Faster Adversarial Robustness Proofs

https://github.com/claimed-framework/component-library

CLAIMED, a visual and scalable component library for Trusted AI

https://github.com/cleverhans-lab/cleverhans/blob/master/cleverhans_v3.1.0/cleverhans/attacks/elastic_net_method.py

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

https://github.com/codelion/adaptive-classifier

Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing

https://github.com/conference-submission-anon/LAT_adversarial_robustness

Robustness May Be at Odds with Accuracy

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

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/csdongxian/csdongxian

Adversarial Weight Perturbation Helps Robust Generalization

https://github.com/ctargon/AdvGAN-tf

Generating Adversarial Examples with Adversarial Networks

https://github.com/ctargon/TSPG

Generating Adversarial Examples with Adversarial Networks

https://github.com/dacostaHugo/Adversarial_attacks

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/davidstutz/disentangling-robustness-generalization

Disentangling Adversarial Robustness and Generalization

https://github.com/deel-ai/deel-torchlip

Achieving robustness in classification using optimal transport with hinge regularization

https://github.com/deel-ai/orthogonium

An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures

https://github.com/dongtsi/TrafficManipulator

Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors

https://github.com/dvlab-research/LBGAT

Decoupled Kullback-Leibler Divergence Loss

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/eth-sri/robust-code

Adversarial Robustness for Code

https://github.com/ethz-spylab/diffusion_denoised_smoothing

(Certified!!) Adversarial Robustness for Free!

https://github.com/evanhu1/ArchitecturalResilienceML

Architectural Resilience to Foreground-and-Background Adversarial Noise

https://github.com/fiona-lxd/AdvKnn

AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients

https://github.com/futakw/Max-Mahalanobis-CenterLoss_pytorch

Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness

https://github.com/gatheluck/Stronghold

Testing Robustness Against Unforeseen Adversaries

https://github.com/git-disl/TOG

Understanding Object Detection Through An Adversarial Lens

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