https://github.com/pantheon5100/deacl
공식 코드Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Papers with Code 아카이브
Adversarial Robustness
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 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/paulgavrikov/adversarial_solarization
공식 코드Don't Look into the Sun: Adversarial Solarization Attacks on Image Classifiers
https://github.com/paulgavrikov/cvpr22w_robustnessthroughthelens
공식 코드Adversarial Robustness through the Lens of Convolutional Filters
https://github.com/paulgavrikov/padding_robustness
공식 코드On the Interplay of Convolutional Padding and Adversarial Robustness
https://github.com/paulviallard/neurips21-pb-robustness
공식 코드https://github.com/pecu/FinancialVision
공식 코드Adversarial Robustness of Deep Convolutional Candlestick Learner
https://github.com/pedrogbmendes/hpt_advtrain
공식 코드https://github.com/peerdavid/gamma-capsule-network
공식 코드Increasing the adversarial robustness and explainability of capsule networks with $γ$-capsules
https://github.com/perturb-spectrum/analysis_and_methods
공식 코드https://github.com/perubhardwaj/attributionattack
공식 코드Adversarial Robustness of Representation Learning for Knowledge Graphs
https://github.com/perubhardwaj/inferenceattack
공식 코드Adversarial Robustness of Representation Learning for Knowledge Graphs
https://github.com/peustr/WCA-net
공식 코드Weight-Covariance Alignment for Adversarially Robust Neural Networks
https://github.com/phibenz/adversarial_ml.research
공식 코드https://github.com/phibenz/robustness_comparison_vit_mlp-mixer_cnn
공식 코드Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
https://github.com/pku-ml/cfa
공식 코드https://github.com/plll4zzx/evaluating-membership-inference-through-adversarial-robustness
공식 코드Evaluating Membership Inference Through Adversarial Robustness
https://github.com/pmichel31415/teapot-nlp
공식 코드On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models
https://github.com/poloclub/robust-principles
공식 코드Robust Principles: Architectural Design Principles for Adversarially Robust CNNs
https://github.com/pralab/Fast-Minimum-Norm-FMN-Attack
공식 코드Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
https://github.com/pralab/HO-FMN
공식 코드Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
https://github.com/pralab/IndicatorsOfAttackFailure
공식 코드Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
https://github.com/pralab/adversarialpruningbenchmark
공식 코드Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness
https://github.com/pralab/imagenet-patch
공식 코드ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
https://github.com/pralab/modsec-advlearn
공식 코드ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning
https://github.com/pralab/modsec-learn
공식 코드ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning
https://github.com/pralab/raid
공식 코드RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
https://github.com/puerrrr/Self-Paced-Adversarial-Training
공식 코드Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting
https://github.com/purdue-nrl/TREND
공식 코드https://github.com/puredefence/advbench4text
공식 코드Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks
https://github.com/qiufan319/benchmark_pc_attack
공식 코드https://github.com/qizhangli/st-at
공식 코드https://github.com/qizhouwang/mail
공식 코드Probabilistic Margins for Instance Reweighting in Adversarial Training
https://github.com/quinn777/cap
공식 코드Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection
https://github.com/rainavyas/attack-comparative-assessment
공식 코드Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment
https://github.com/raphaelolivier/sparsity
공식 코드How many perturbations break this model? Evaluating robustness beyond adversarial accuracy
https://github.com/rcpsl/deepbern-nets
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.