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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/Monoshi-tonmoy/Evaluating-LLMs-on-Hate-Speech-Detection

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Evaluation of Hate Speech Detection Using Large Language Models and Geographical Contextualization

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

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A Self-supervised Approach for Adversarial Robustness

https://github.com/NISP-official/CARTL

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CARTL: Cooperative Adversarially-Robust Transfer Learning

https://github.com/NeurAI-Lab/ACT

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Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural Networks

https://github.com/NogaBar/mr_robust_optim

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Multiplicative Reweighting for Robust Neural Network Optimization

https://github.com/P2333/Adaptive-Diversity-Promoting

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Improving Adversarial Robustness via Promoting Ensemble Diversity

https://github.com/P2333/Bag-of-Tricks-for-AT

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Bag of Tricks for Adversarial Training

https://github.com/P2333/Max-Mahalanobis-Training

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Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness

https://github.com/P2333/Mixup-Inference

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Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks

https://github.com/PaddlePaddle/InterpretDL

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Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond

https://github.com/Pehlevan-Group/rep-spectral

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Spectral regularization for adversarially-robust representation learning

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

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Visual Prompting for Adversarial Robustness

https://github.com/Rasmuskh/dualprop_icml_2024

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Two Tales of Single-Phase Contrastive Hebbian Learning

https://github.com/RobustBench/robustbench

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RobustBench: a standardized adversarial robustness benchmark

https://github.com/RoyalSkye/ATCL

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Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks

https://github.com/RulinShao/on-the-adversarial-robustness-of-visual-transformer

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On the Adversarial Robustness of Vision Transformers

https://github.com/RyanHaoranLi/CAR-RL

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Towards Optimal Adversarial Robust Reinforcement Learning with Infinity Measurement Error

https://github.com/SCAlabUnical/CLEAR-Bias_LLM_benchmark

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Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-Judge

https://github.com/SCAlabUnical/LLM-Bias-Jailbreak

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Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias Elicitation

https://github.com/SCLBD/BlackboxBench

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Random Noise Defense Against Query-Based Black-Box Attacks

https://github.com/SaFoLab-WISC/OET

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OET: Optimization-based prompt injection Evaluation Toolkit

https://github.com/Saehyung-Lee/cifar10_challenge

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Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization

https://github.com/SarielMa/ICLR2020_AI4AH

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Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective

https://github.com/SarielMa/Increasing-Margin-Adversarial-Training

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Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks

https://github.com/ShawnXYang/Face-Robustness-Benchmark

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RobFR: Benchmarking Adversarial Robustness on Face Recognition

https://github.com/SongweiGe/shift-invariance-adv-robustness

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Shift Invariance Can Reduce Adversarial Robustness

https://github.com/Sulam-Group/Adversarial-Robust-Supervised-Sparse-Coding

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Adversarial Robustness of Supervised Sparse Coding

https://github.com/Sxing2/CLIP-Test-time-Counterattacks

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CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP

https://github.com/TAMU-VITA/Adv-SS-Pretraining

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Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

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

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Toward Adversarial Robustness via Semi-supervised Robust Training

https://github.com/TSKumarage/J-Guard

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J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News

https://github.com/TienjinHuang/WOT

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Enhancing Adversarial Training via Reweighting Optimization Trajectory

https://github.com/TongCHEN779/CertDNN

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Semialgebraic Optimization for Lipschitz Constants of ReLU Networks

https://github.com/Tsili42/adv-ntk

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What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?

https://github.com/TsukamotoShuchi/ROCK

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Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks

https://github.com/UMBCvision/Contextual-Adversarial-Patches

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Role of Spatial Context in Adversarial Robustness for Object Detection

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