Topic: adversarial-robustness Goto Github
Some thing interesting about adversarial-robustness
Some thing interesting about adversarial-robustness
adversarial-robustness,[TMLR 2023] as a featured article (spotlight :star2: or top 0.01% of the accepted papers). In this study, we systematically examine the robustness of both traditional and learned perceptual similarity metrics to imperceptible adversarial perturbations.
User: abhijay9
Home Page: https://openreview.net/forum?id=r9vGSpbbRO
adversarial-robustness,[ICLR 2021] "InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective" by Boxin Wang, Shuohang Wang, Yu Cheng, Zhe Gan, Ruoxi Jia, Bo Li, Jingjing Liu
Organization: ai-secure
adversarial-robustness,PyTorch implementation of Targeted Adversarial Perturbations for Monocular Depth Predictions (in NeurIPS 2020)
User: alexklwong
adversarial-robustness,EasyRobust: an Easy-to-use library for state-of-the-art Robust Computer Vision Research with PyTorch.
Organization: alibaba
adversarial-robustness,Implementation of the paper "Improving the Accuracy-Robustness Trade-off of Classifiers via Adaptive Smoothing".
User: bai-yt
Home Page: https://arxiv.org/abs/2301.12554
adversarial-robustness,MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers
User: bai-yt
adversarial-robustness,Adversarial Attack and Defense in Deep Ranking, T-PAMI, 2024
User: cdluminate
Home Page: https://arxiv.org/abs/2106.03614
adversarial-robustness,Lipschitz Neural Networks described in "Sorting Out Lipschitz Function Approximation" (ICML 2019).
User: cemanil
adversarial-robustness,[ECCV 2024 - Oral] Official PyTorch Implementation of "Adversarial Robustification via Text-to-Image Diffusion Models"
User: choidae1
adversarial-robustness,👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)
User: emaballarin
Home Page: https://arxiv.org/abs/2306.06081
adversarial-robustness,[ICLR 2022] Boosting Randomized Smoothing with Variance Reduced Classifiers
Organization: eth-sri
Home Page: https://arxiv.org/abs/2106.06946
adversarial-robustness,Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
User: fra31
Home Page: https://arxiv.org/abs/2003.01690
adversarial-robustness,Code for FAB-attack
User: fra31
adversarial-robustness,Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"
User: fra31
Home Page: https://arxiv.org/abs/2105.12508
adversarial-robustness,[ICML 2023] "NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations" by Yonggan Fu, Ye Yuan, Souvik Kundu, Shang Wu, Shunyao Zhang, Yingyan (Celine) Lin
Organization: gatech-eic
adversarial-robustness,[ICLR 2022] "Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?" by Yonggan Fu, Shunyao Zhang, Shang Wu, Cheng Wan, Yingyan Lin
Organization: gatech-eic
adversarial-robustness,Feature Scattering Adversarial Training (NeurIPS19)
User: haichao-zhang
adversarial-robustness,Fantastic Robustness Measures: The Secrets of Robust Generalization [NeurIPS 2023]
User: harry24k
Home Page: https://openreview.net/forum?id=AGVBqJuL0T
adversarial-robustness,Code for "Improving Robustness Against Stealthy Weight Bit-Flip Attacks by Output Code Matching" [CVPR 2022]
Organization: igitugraz
adversarial-robustness,Code for "Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling" [ICML 2021]
Organization: igitugraz
adversarial-robustness,Unofficial implementation of the DeepMind papers "Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples" & "Fixing Data Augmentation to Improve Adversarial Robustness" in PyTorch
User: imrahulr
adversarial-robustness,Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
User: imrahulr
adversarial-robustness,Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness. (MD attacks)
User: jack-lx-jiang
adversarial-robustness,Decoupled Kullback-Leibler Divergence Loss (DKL)
User: jiequancui
Home Page: https://arxiv.org/pdf/2305.13948v1.pdf
adversarial-robustness,LAFEAT: Piercing Through Adversarial Defenses with Latent Features (CVPR 2021 Oral)
Organization: lafeat
adversarial-robustness,[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training
User: layneh
adversarial-robustness,Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]
User: max-andr
Home Page: https://arxiv.org/abs/1912.00049
adversarial-robustness,Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
Organization: microsoft
Home Page: https://arxiv.org/abs/2003.01908
adversarial-robustness,[ECCV 2020 AROW Workshop] A Deep Dive into Adversarial Robustness in Zero-Shot Learning
User: mkyucel
adversarial-robustness,📕 Adversarial Attacks and Defenses for Image-Based Recommendation Systems using Deep Neural Networks.
User: philippnormann
adversarial-robustness,RobustBench: a standardized adversarial robustness benchmark [NeurIPS'21 Benchmarks and Datasets Track]
Organization: robustbench
Home Page: https://robustbench.github.io
adversarial-robustness,[NeurIPS2020] The official repository of "Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks".
User: samuel930930
adversarial-robustness,This repository contains the official implementation of the paper "Reliable Graph Neural Networks via Robust Aggregation" (NeurIPS, 2020).
User: sigeisler
Home Page: https://www.in.tum.de/daml/reliable-gnn-via-robust-aggregation/
adversarial-robustness,A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.
Organization: thu-ml
Home Page: https://thu-ml-ares.rtfd.io
adversarial-robustness,alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)
Organization: verified-intelligence
adversarial-robustness,[CVPR 2020] Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Organization: vita-group
adversarial-robustness,[ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chang, Zhangyang Wang
Organization: vita-group
adversarial-robustness,[CVPR 2022] "Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations" by Tianlong Chen*, Peihao Wang*, Zhiwen Fan, Zhangyang Wang
Organization: vita-group
adversarial-robustness,[TMLR 22] "Queried Unlabeled Data Improves and Robustifies Class- Incremental Learning" by Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Animi, Zhangyang Wang
Organization: vita-group
adversarial-robustness,[ICML 2022] "Data-Efficient Double-Win Lottery Tickets from Robust Pre-training" by Tianlong Chen, Zhenyu Zhang, Sijia Liu, Yang Zhang, Shiyu Chang, Zhangyang Wang
Organization: vita-group
adversarial-robustness,[TMLR] "Can You Win Everything with Lottery Ticket?" by Tianlong Chen, Zhenyu Zhang, Jun Wu, Randy Huang, Sijia Liu, Shiyu Chang, Zhangyang Wang
Organization: vita-group
adversarial-robustness,[ICML 2021 Long Talk] "Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm" by Mingkang Zhu, Tianlong Chen, Zhangyang Wang
Organization: vita-group
adversarial-robustness,[ICLR 2020] ”Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference“
Organization: vita-group
adversarial-robustness,Implementing the algorithm from our paper: "A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning".
User: xinyiys
adversarial-robustness,Connecting Interpretability and Robustness in Decision Trees through Separation
User: yangarbiter
Home Page: https://arxiv.org/abs/2102.07048
adversarial-robustness,[ICML 2021] This is the official github repo for training L_inf dist nets with high certified accuracy.
User: zbh2047
adversarial-robustness,[ICLR 2022] Training L_inf-dist-net with faster acceleration and better training strategies
User: zbh2047
adversarial-robustness,Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective
User: zhichao-lu
adversarial-robustness,[NeurIPS'20 Oral] DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles
User: zjysteven
adversarial-robustness,A privacy attack that exploits Adversarial Training models to compromise the privacy of Federated Learning systems.
User: zjysteven
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