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Adversarial Robustness

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하위 태스크: 0
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/stanislavfort/adversaries_to_ood_detection

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Adversarial vulnerability of powerful near out-of-distribution detection

https://github.com/stanislavfort/ensemble-everything-everywhere

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Ensemble everything everywhere: Multi-scale aggregation for adversarial robustness

https://github.com/star-ailab/armor

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Adversarially Robust Learning with Optimal Transport Regularized Divergences

https://github.com/star4455/graphre

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Understanding the Robustness of Graph Neural Networks against Adversarial Attacks

https://github.com/stonezwr/TSSL-BP

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HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds

https://github.com/suhyeok24/ft-cadis

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Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness

https://github.com/szegedai/robust-segmentation-evaluation

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Evaluating the Adversarial Robustness of Semantic Segmentation: Trying Harder Pays Off

https://github.com/tahabelkhouja/dtw-ar

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Dynamic Time Warping based Adversarial Framework for Time-Series Domain

https://github.com/tdchaitanya/nas-robustness

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On Adversarial Robustness: A Neural Architecture Search perspective

https://github.com/techtrekkerz/fsu

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Feature Statistics with Uncertainty Help Adversarial Robustness

https://github.com/thib-s/orthogonium

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An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures

https://github.com/thobauma/aadefdino

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Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO

https://github.com/thomasp05/improved-robustness-with-ecoc

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Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes

https://github.com/thu-ml/ares

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Adversarial Attacks on ML Defense Models Competition

https://github.com/thu-ml/at3d

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Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition

https://github.com/thu-ml/attack-bard

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How Robust is Google's Bard to Adversarial Image Attacks?

https://github.com/thu-ml/oddefense

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On the Importance of Backbone to the Adversarial Robustness of Object Detectors

https://github.com/thudm/grb

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Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

https://github.com/thunlp/MixADA

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Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning

https://github.com/tianzheng4/Distributionally-Adversarial-Attack

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Towards Sharper First-Order Adversary with Quantized Gradients

https://github.com/tj-kim/pfeddef_v1

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Characterizing Internal Evasion Attacks in Federated Learning

https://github.com/tly18/tpap

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Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM

https://github.com/tmllab/2023_neurips_aaer

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Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization

https://github.com/tobias-fllnr/robustnessgeneralizationqml

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The interplay of robustness and generalization in quantum machine learning

https://github.com/tomsheng21/r-tpt

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R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning

https://github.com/trais-lab/adversarial-attack-data-attribution

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Adversarial Attacks on Data Attribution

https://github.com/treelli/apt

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One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models

https://github.com/treelli/aroid

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AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation

https://github.com/treelli/instance-adaptive-smoothness-enhanced-at

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Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing

https://github.com/trustai/loat

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Boosting Adversarial Training via Fisher-Rao Norm-based Regularization

https://github.com/trustai/ode4robustvit

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Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem

https://github.com/trustai/rerogcrl

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ReRoGCRL: Representation-based Robustness in Goal-Conditioned Reinforcement Learning

https://github.com/trustaiot/lrs

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LRS: Enhancing Adversarial Transferability through Lipschitz Regularized Surrogate

https://github.com/tsbiosky/PointACL

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PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial Attack

https://github.com/tsingqguo/irad

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IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks

https://github.com/tuananhbui89/Adversarial-Divergence-Reduction

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Improving Adversarial Robustness by Enforcing Local and Global Compactness

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