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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/VITA-Group/Adversarial-Contrastive-Learning

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Robust Pre-Training by Adversarial Contrastive Learning

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/YyzHarry/ME-Net

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ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation

https://github.com/ZhengyuZhao/ACE

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Adversarial Image Color Transformations in Explicit Color Filter Space

https://github.com/ZiangYan/deepdefense.pytorch

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Deep Defense: Training DNNs with Improved Adversarial Robustness

https://github.com/a1noack/interp_regularization

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An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness

https://github.com/aam-at/cpgd

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PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack

https://github.com/accuracy-disparity/at-on-ad

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Understanding the Impact of Adversarial Robustness on Accuracy Disparity

https://github.com/acmi-lab/mvp

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Model-tuning Via Prompts Makes NLP Models Adversarially Robust

https://github.com/adirrahamim/claf

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Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training

https://github.com/advcvpr23/adversarial-mini-batching

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Generating Adversarial Samples in Mini-Batches May Be Detrimental To Adversarial Robustness

https://github.com/adverml/advopenood

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Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors

https://github.com/adverml/multilid

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Unfolding Local Growth Rate Estimates for (Almost) Perfect Adversarial Detection

https://github.com/adverml/spectraldef_framework

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Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

https://github.com/adversarial-for-goodness/Co-Attack

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Towards Adversarial Attack on Vision-Language Pre-training Models

https://github.com/ahmedshah1494/speech_robust_bench

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Speech Robust Bench: A Robustness Benchmark For Speech Recognition

https://github.com/ai-secure/layerwise-orthogonal-training

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LOT: Layer-wise Orthogonal Training on Improving $\ell_2$ Certified Robustness

https://github.com/ai4life-group/average-case-robustness

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Characterizing Data Point Vulnerability via Average-Case Robustness

https://github.com/aika-wsd/KGPA

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KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs

https://github.com/aim-harvard/sdoh

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Large Language Models to Identify Social Determinants of Health in Electronic Health Records

https://github.com/aipenguin/stopreasoning

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Stop Reasoning! When Multimodal LLM with Chain-of-Thought Reasoning Meets Adversarial Image

https://github.com/aiproj/towards-out-of-distribution-adversarial-robustness

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Towards Out-of-Distribution Adversarial Robustness

https://github.com/akshaymehra24/poisoning_certified_defenses

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How Robust are Randomized Smoothing based Defenses to Data Poisoning?

https://github.com/alessandrodepalma/expressive-losses

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Expressive Losses for Verified Robustness via Convex Combinations

https://github.com/alessandrodepalma/ibpr

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IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound

https://github.com/alevine0/fractionallprobustness

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Provable Adversarial Robustness for Fractional Lp Threat Models

https://github.com/alexkoulakos/explain-then-predict

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Enhancing adversarial robustness in Natural Language Inference using explanations

https://github.com/ali-e/fastclip

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Spectrum Extraction and Clipping for Implicitly Linear Layers

https://github.com/alicedoherty/bayesian-fairness

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Individual Fairness in Bayesian Neural Networks

https://github.com/alinlab/consistency-adversarial

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Consistency Regularization for Adversarial Robustness

https://github.com/alinlab/smoothing-catrs

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Confidence-aware Training of Smoothed Classifiers for Certified Robustness

https://github.com/alirezaabdollahpour/superdeepfool

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Revisiting DeepFool: generalization and improvement

https://github.com/alvinchangw/IGAM_CVPR2020

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What it Thinks is Important is Important: Robustness Transfers through Input Gradients

https://github.com/amazon-research/adversarial-robustness-with-nonuniform-perturbations

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Adversarial Robustness with Non-uniform Perturbations

https://github.com/amazon-research/assaying-ood

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Assaying Out-Of-Distribution Generalization in Transfer Learning

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