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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/columbia/Metric_Learning_Adversarial_Robustness

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Metric Learning for Adversarial Robustness

https://github.com/cornell-zhang/garnet

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GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks

https://github.com/csdongxian/AWP

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Adversarial Weight Perturbation Helps Robust Generalization

https://github.com/cv-stuttgart/detectiondefenses

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Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow

https://github.com/cv-stuttgart/pcfa

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A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical Flow

https://github.com/cvlab-columbia/equi4rob

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Robust Perception through Equivariance

https://github.com/cvlab-columbia/zsrobust4foundationmodel

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Understanding Zero-Shot Adversarial Robustness for Large-Scale Models

https://github.com/cyclebooster/unsupervised-adversarial-detection-without-extra-model

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Unsupervised Adversarial Detection without Extra Model: Training Loss Should Change

https://github.com/cyr19/menli

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MENLI: Robust Evaluation Metrics from Natural Language Inference

https://github.com/d12306/dsnet

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Learning Diverse-Structured Networks for Adversarial Robustness

https://github.com/damon-demon/black-box-defense

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How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective

https://github.com/daniel-fink-de/training-robust-and-generalizable-quantum-models

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Training robust and generalizable quantum models

https://github.com/daniel-xsy/bev-attack

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On the Adversarial Robustness of Camera-based 3D Object Detection

https://github.com/darrenzhang01/excon

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ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification

https://github.com/davidstutz/cvpr2019-adversarial-robustness

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Disentangling Adversarial Robustness and Generalization

https://github.com/ddkang/advex-uar

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Testing Robustness Against Unforeseen Adversaries

https://github.com/declipsonator/gptzzzs

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Authorship Obfuscation in Multilingual Machine-Generated Text Detection

https://github.com/dedeswim/timm-adv-training

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Scaling Compute Is Not All You Need for Adversarial Robustness

https://github.com/dedeswim/vits-robustness-torch

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A Light Recipe to Train Robust Vision Transformers

https://github.com/deel-ai/deel-lip

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Achieving robustness in classification using optimal transport with hinge regularization

https://github.com/deep-kaixun/videopure

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VideoPure: Diffusion-based Adversarial Purification for Video Recognition

https://github.com/deepgenerativeclassifier/DeepBayes

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Are Generative Classifiers More Robust to Adversarial Attacks?

https://github.com/deepmind/deepmind-research

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Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

https://github.com/deepmind/deepmind-research/tree/master/unsupervised_adversarial_training

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Are Labels Required for Improving Adversarial Robustness?

https://github.com/deepmind/jax_verify

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Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications

https://github.com/deepsota/astfocus

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Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos

https://github.com/detecting-by-dissecting/detecting-by-dissecting

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An Adversarial Robustness Perspective on the Topology of Neural Networks

https://github.com/dineshsrivasthav/adaptive_meta_learning_with_multi_agent_framework

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Adaptive Meta-Learning for Robust Deepfake Detection: A Multi-Agent Framework to Data Drift and Model Generalization

https://github.com/distrinet/flowpure

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FlowPure: Continuous Normalizing Flows for Adversarial Purification

https://github.com/divyam3897/ANP_VS

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Adversarial Neural Pruning with Latent Vulnerability Suppression

https://github.com/dongxinshuai/rift-neurips2021

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How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?

https://github.com/dongxzz/roli

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Initialization Matters for Adversarial Transfer Learning

https://github.com/dorcoh/nnsynthesizer

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Automated Repair of Neural Networks

https://github.com/dravyanshsharma/adversarial-contrastive

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An Analysis of Robustness of Non-Lipschitz Networks

https://github.com/dtak/adversarial-robustness-public

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Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients

https://github.com/dwdavidxd/miat

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Improving Adversarial Robustness via Mutual Information Estimation

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