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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/francesco-campi/rob-subgraphs

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Expressivity of Graph Neural Networks Through the Lens of Adversarial Robustness

https://github.com/fraunhofer-iais/Robust-Entropy-Search

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Robust Entropy Search for Safe Efficient Bayesian Optimization

https://github.com/fraunhoferiee/taaowpf

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Targeted Adversarial Attacks on Wind Power Forecasts

https://github.com/fredwu-hust/dd-robustbench

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DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation

https://github.com/ftramer/MultiRobustness

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Adversarial Training and Robustness for Multiple Perturbations

https://github.com/gatech-eic/nerfool

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NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations

https://github.com/gejulia/robustness_confidences_evaluation

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Robust Models are less Over-Confident

https://github.com/georgsiedel/adversarial-distance-estimation

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A practical approach to evaluating the adversarial distance for machine learning classifiers

https://github.com/georgsiedel/lp-norm-corruption-robustness

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Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions

https://github.com/geosada/LVAT

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Regularization with Latent Space Virtual Adversarial Training

https://github.com/gihyunkim/Exploring-Adversarial-Robustness-of-ViT-in-the-Spectral-Perspective

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Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective

https://github.com/giladcohen/arf

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Simple Post-Training Robustness Using Test Time Augmentations and Random Forest

https://github.com/giladcohen/katana

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Simple Post-Training Robustness Using Test Time Augmentations and Random Forest

https://github.com/goldblum/AdversariallyRobustDistillation

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Adversarially Robust Distillation

https://github.com/google-research/mnist-c

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MNIST-C: A Robustness Benchmark for Computer Vision

https://github.com/gq-max/advdiffvlm

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Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models

https://github.com/grayswanai/circuit-breakers

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Improving Alignment and Robustness with Circuit Breakers

https://github.com/grispeut/Feature-Alignment

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Using Feature Alignment Can Improve Clean Average Precision and Adversarial Robustness in Object Detection

https://github.com/grispeut/udfa

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Robust and Accurate Object Detection via Self-Knowledge Distillation

https://github.com/guanlinlee/oat

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Omnipotent Adversarial Training in the Wild

https://github.com/guoyongcs/ews

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Improving Robustness by Enhancing Weak Subnets

https://github.com/guruprasad93/flexiblemachinelearning

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Engineering flexible machine learning systems by traversing functionally-invariant paths

https://github.com/gvcl/iwseg-sar-poison

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Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images

https://github.com/hamidbostani2021/robust-feature-space

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Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations

https://github.com/han8931/rsmi_nlp

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Randomized Smoothing with Masked Inference for Adversarially Robust Text Classifications

https://github.com/hanshuyan/cifs

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CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection

https://github.com/hanxunh/mdattack

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Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness

https://github.com/harrywuhust2022/Adv-Crowd-analysis

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Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting

https://github.com/hashmatshadab/mambarobustness

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Towards Evaluating the Robustness of Visual State Space Models

https://github.com/hashmatshadab/robust-llava

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Robust-LLaVA: On the Effectiveness of Large-Scale Robust Image Encoders for Multi-modal Large Language Models

https://github.com/hashmatshadab/robustness-of-volumetric-medical-segmentation-models

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On Evaluating Adversarial Robustness of Volumetric Medical Segmentation Models

https://github.com/havelhuang/hda-testing

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Hierarchical Distribution-Aware Testing of Deep Learning

https://github.com/helloworld10011/adversarial-reasoning

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Adversarial Reasoning at Jailbreaking Time

https://github.com/hendrycks/pre-training

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Using Pre-Training Can Improve Model Robustness and Uncertainty

https://github.com/hendrycks/robustness

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Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning

https://github.com/hfmei/idc

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Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness

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