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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/wi-pi/stochastic-preprocessing-defenses

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On the Limitations of Stochastic Pre-processing Defenses

https://github.com/williamberrios/BrainScore-Transformers

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Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4

https://github.com/wjun0830/localizable-rotation

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Tailoring Self-Supervision for Supervised Learning

https://github.com/wkim97/fsr

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Feature Separation and Recalibration for Adversarial Robustness

https://github.com/wowotou1998/exploring-adv-by-mutual-info

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Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information

https://github.com/wutong16/Adversarial_Long-Tail

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Adversarial Robustness under Long-Tailed Distribution

https://github.com/x1716/iqa-adapter

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IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative Models

https://github.com/xandery-geek/SAAT

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Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval

https://github.com/xiaohangt/ardt

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Adversarially Robust Decision Transformer

https://github.com/xiaohulugo/ScAR-Scaling-Adversarial-Robustness-for-LiDAR-Object-Detection

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ScAR: Scaling Adversarial Robustness for LiDAR Object Detection

https://github.com/xiaohulugo/scar-iros2023

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ScAR: Scaling Adversarial Robustness for LiDAR Object Detection

https://github.com/xiaoyuluoit97/DeMem

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DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization

https://github.com/xiaoyunxxy/ib-rar

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IB-RAR: Information Bottleneck as Regularizer for Adversarial Robustness

https://github.com/xiaoyunxxy/mimir

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MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness

https://github.com/xiaozhanguva/Intrinsic-Rob

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Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models

https://github.com/xiaozhanguva/intrinsic_rob_lu

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Understanding Intrinsic Robustness Using Label Uncertainty

https://github.com/xingchenwan/grabnel

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Adversarial Attacks on Graph Classification via Bayesian Optimisation

https://github.com/xinli0928/PC-LC

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Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints

https://github.com/xuwangyin/at-ebms

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Learning Energy-Based Models With Adversarial Training

https://github.com/yaircarmon/semisup-adv

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Unlabeled Data Improves Adversarial Robustness

https://github.com/yamizi/taskaugment

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GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks

https://github.com/yangarbiter/nearest-category-generalization

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Probing Predictions on OOD Images via Nearest Categories

https://github.com/yangid/defenseiqa-nt

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Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization

https://github.com/yao-lab/SONet

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Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients

https://github.com/yaodongyu/TRADES

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Theoretically Principled Trade-off between Robustness and Accuracy

https://github.com/yeonjun-in/torch-sp-agcl

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https://github.com/yeshaokai/Robustness-Aware-Pruning-ADMM

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Adversarial Robustness vs Model Compression, or Both?

https://github.com/yjhuangcd/CNNF

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Neural Networks with Recurrent Generative Feedback

https://github.com/yjhuangcd/fi-ode

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FI-ODE: Certifiably Robust Forward Invariance in Neural ODEs

https://github.com/yolky/adversarial_ntk_evolution

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Evolution of Neural Tangent Kernels under Benign and Adversarial Training

https://github.com/yonggangzhangustc/causaladv

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CausalAdv: Adversarial Robustness through the Lens of Causality

https://github.com/youweiliang/norm_robustness

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Large Norms of CNN Layers Do Not Hurt Adversarial Robustness

https://github.com/youzunzhi/nim-advdef

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Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense

https://github.com/yqy2001/a-infonce

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Adversarial Contrastive Learning via Asymmetric InfoNCE

https://github.com/ysharma1126/EAD-Attack

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EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

https://github.com/ysharma1126/caad_18

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CAAD 2018: Generating Transferable Adversarial Examples

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