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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/BaoWangMath/DNN-DataDependentActivation

공식 코드

Adversarial Defense via Data Dependent Activation Function and Total Variation Minimization

https://github.com/BaoWangMath/EnResNet

공식 코드

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

https://github.com/BorealisAI/advertorch

공식 코드

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

https://github.com/BorealisAI/mma_training

공식 코드

MMA Training: Direct Input Space Margin Maximization through Adversarial Training

https://github.com/BouriMohammed/GBM

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Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach

https://github.com/BruceResearch/mFI-PSO

공식 코드

mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks

https://github.com/Chenglin-Yang/PatchAttack

공식 코드

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

https://github.com/EricDai0/advdiff

공식 코드

AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models

https://github.com/Garsonjw/AdvFAS

공식 코드

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

https://github.com/Gnomy17/robust_prompt

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ADAPT to Robustify Prompt Tuning Vision Transformers

https://github.com/Guneet-Dhillon/Stochastic-Activation-Pruning

공식 코드

Stochastic Activation Pruning for Robust Adversarial Defense

https://github.com/Hadisalman/smoothing-adversarial

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Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers

https://github.com/HaimFisher/fingerprints-armor

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Neural Fingerprints for Adversarial Attack Detection

https://github.com/JunweiLiang/Multiverse/tree/master/SimAug

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SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras

https://github.com/KaidiXu/auto_LiRPA

공식 코드

Fast Certified Robust Training with Short Warmup

https://github.com/MAGICS-LAB/GenoArmory

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GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models

https://github.com/MadryLab/cifar10_challenge

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Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/MadryLab/mnist_challenge

공식 코드

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/MarinePICOT/Adversarial-Robustness-via-Fisher-Rao-Regularization

공식 코드

Adversarial Robustness via Fisher-Rao Regularization

https://github.com/Muzammal-Naseer/SAT

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Stylized Adversarial Defense

https://github.com/NVlabs/DiffPure

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Diffusion Models for Adversarial Purification

https://github.com/NikolaiKyhne/Detecting-and-Defending-Against-Adversarial-Attacks

공식 코드

Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion Models

https://github.com/OPTML-Group/DeepZero

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DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training

https://github.com/Phoveran/vp-for-adversarial-robustness

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Visual Prompting for Adversarial Robustness

https://github.com/QData/AdversarialDNN-Playground

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Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning

https://github.com/Shawn-Shan/trapdoor

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Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks

https://github.com/Shorya-Sharma/Game-Theory-For-Adversarial-Attacks-And-Defenses

공식 코드

Game Theory for Adversarial Attacks and Defenses

https://github.com/Shreyasi2002/Adversarial_Attack_Defense

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Defense Against Adversarial Attacks using Convolutional Auto-Encoders

https://github.com/THUYimingLi/Semi-supervised_Robust_Training

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Toward Adversarial Robustness via Semi-supervised Robust Training

https://github.com/TrustAI/DeepGame

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A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees

https://github.com/Trustworthy-ML-Lab/Interpretability-Guided-Defense

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Interpretability-Guided Test-Time Adversarial Defense

https://github.com/VITA-Group/Sandwich-Batch-Normalization

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Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity

https://github.com/VeriDeep/DLV

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Safety Verification of Deep Neural Networks

https://github.com/Visual-Conception-Group/LGAP

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Language Guided Adversarial Purification

https://github.com/XinyiYS/Robust-and-Fair-Federated-Learning

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

https://github.com/ZhangJC-2k/RandomSampling

공식 코드

Random Sampling for Diffusion-based Adversarial Purification

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