Papers with Code 아카이브

Adversarial Defense

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

10

보존된 평가 기록

28

코드

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

https://github.com/stellargraph/stellargraph

공식 코드

Adversarial Examples on Graph Data: Deep Insights into Attack and Defense

https://github.com/sunblaze-ucb/transferability-advdnn-pub

공식 코드

Delving into Transferable Adversarial Examples and Black-box Attacks

https://github.com/tensorflow/cleverhans

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Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

https://github.com/thudzj/ScalableBDL

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LiBRe: A Practical Bayesian Approach to Adversarial Detection

https://github.com/thuhcsi/adsv_voting

공식 코드

Voting for the right answer: Adversarial defense for speaker verification

https://github.com/tly18/tpap

공식 코드

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

https://github.com/tmlr-group/dad

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One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy

https://github.com/trustmlrg/cs-rs

공식 코드

Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing

https://github.com/tsachiblau/Threat-Model-Agnostic-Adversarial-Defense-using-Diffusion-Models

공식 코드

Threat Model-Agnostic Adversarial Defense using Diffusion Models

https://github.com/tsborkar/Selective-feature-regeneration

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Defending Against Universal Attacks Through Selective Feature Regeneration

https://github.com/ucsb-nlp-chang/textgrad

공식 코드

TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization

https://github.com/val-iisc/GAMA-GAT

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Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses

https://github.com/val-iisc/oaat

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Scaling Adversarial Training to Large Perturbation Bounds

https://github.com/vcl-iisc/data-free-defense-at-test-time

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DAD++: Improved Data-free Test Time Adversarial Defense

https://github.com/vcl-iisc/improved-data-free-test-time-adversarial-defense

공식 코드

DAD++: Improved Data-free Test Time Adversarial Defense

https://github.com/wang-axis/dna_ecg

공식 코드

Decorrelative Network Architecture for Robust Electrocardiogram Classification

https://github.com/wangren09/RAILS

공식 코드

RAILS: A Robust Adversarial Immune-inspired Learning System

https://github.com/wangren09/mcgr

공식 코드

Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified $\ell_p$ Attacks

https://github.com/wenbinlee/defensivefsl

공식 코드

Defensive Few-shot Learning

https://github.com/winycg/HCGNet

공식 코드

Gated Convolutional Networks with Hybrid Connectivity for Image Classification

https://github.com/wwoods/adversarial-explanations-cifar

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Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness

https://github.com/xiaosen-wang/SEM

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Natural Language Adversarial Defense through Synonym Encoding

https://github.com/xingjunm/lid_adversarial_subspace_detection

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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality

https://github.com/xqchen914/abat

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Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs

https://github.com/xuanqing94/BayesianDefense

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Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network

https://github.com/xunguangwang/cgat

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CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval

https://github.com/xuwangyin/at-ebms

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Learning Energy-Based Models With Adversarial Training

https://github.com/yanemcovsky/SIAM

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Smoothed Inference for Adversarially-Trained Models

https://github.com/yangarbiter/adversarial-nonparametrics

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Robustness for Non-Parametric Classification: A Generic Attack and Defense

https://github.com/yangheng95/PyABSA/blob/release/demos/aspect_polarity_classification/run_fast_lsa_deberta_large.py

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LSA: Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation

https://github.com/yangyi-chen/lm-toast

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Making Pre-trained Language Models both Task-solvers and Self-calibrators

https://github.com/yao-lab/SONet

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Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients

https://github.com/yaodongyu/TRADES

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Theoretically Principled Trade-off between Robustness and Accuracy

https://github.com/yi126/lightweight-defence

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Lightweight Defense Against Adversarial Attacks in Time Series Classification

https://github.com/yossilevii100/critical_points2

공식 코드

Robustifying Point Cloud Networks by Refocusing

https://github.com/yossilevii100/refocusing

공식 코드

Robustifying Point Cloud Networks by Refocusing

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