68 Open source projects that are alternatives of or similar to robust-local-lipschitz

Adversarial Distributional Training (NeurIPS 2020)

NIPS Adversarial Vision Challenge

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

Scaleable input gradient regularization

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

Task-agnostic universal black-box attacks on computer vision neural network via procedural noise (CCS'19)

A curated list of awesome resources regarding machine learning reliability.

A curated (most recent) list of resources for Learning with Noisy Labels

Reachability analysis for closed-loop control systems

My entry for ICLR 2018 Reproducibility Challenge for paper Synthesizing robust adversarial examples https://openreview.net/pdf?id=BJDH5M-AW

PyTorch Implementation of Google's Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions. This implementation supports both single-, multi-speaker TTS and several techniques to enforce the robustness and efficiency of the model.

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.

Code for NeurIPS 2019 Paper

Recentrifuge: robust comparative analysis and contamination removal for metagenomics

Benchmarks for Corruption Invariant Person Re-identification. [NeurIPS 2021 Track on Datasets and Benchmarks]

Code for our CVPR 2018 paper, "On the Robustness of Semantic Segmentation Models to Adversarial Attacks"

Generative Forests in Python

Code to reproduce the attacks and defenses for the entries "JeromeR" in the NIPS 2018 Adversarial Vision Challenge

[NeurIPS'2019] Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu, “Model Compression with Adversarial Robustness: A Unified Optimization Framework”

Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs

Robust error-handling for Kotlin and Android

SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression

RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)

[CVPR 2022] S-attack library. Official implementation of two papers "Vehicle trajectory prediction works, but not everywhere" and "Are socially-aware trajectory prediction models really socially-aware?".

Code for the unrestricted adversarial examples paper (NeurIPS 2018)

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Minimal implementation of Denoised Smoothing (https://arxiv.org/abs/2003.01908) in TensorFlow.

Code used during my Chaos Engineering and Resiliency Patterns talk.

[NeurIPS 2021]: Are Transformers More Robust Than CNNs? (Pytorch implementation & checkpoints)

reproduction of Thermometer Encoding: One Hot Way To Resist Adversarial Examples in pytorch

Attacking Vision based Perception in End-to-end Autonomous Driving Models

Implementation of the paper "Certifiable Robustness and Robust Training for Graph Convolutional Networks".

Contains code for the paper "Vision Transformers are Robust Learners" (AAAI 2022).

Source code for the ICLR 2021 work "Generating Adversarial Computer Programs using Optimized Obfuscations"

Implemention of Fast Gradient Sign Method for generating adversarial examples in Keras

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL

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

Python toolbox to evaluate graph vulnerability and robustness (CIKM 2021)

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

Athena: A Framework for Defending Machine Learning Systems Against Adversarial Attacks

Source code and dataset for the CCKS201 paper "On Robustness and Bias Analysis of BERT-based Relation Extraction"

Randomized Smoothing of All Shapes and Sizes (ICML 2020).

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation （ICCV2021）

adversarial examples, adversarial malware examples, adversarial malware detection, adversarial deep ensemble, Android malware variants

Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".

Sensor data aggregation tool for any numerical sensor data. Robust and privacy-friendly.

Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)

The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-…

Robustness benchmark for DNN models.

Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

Adversarial Ranking Attack and Defense, ECCV, 2020.

This is our official implementation for the paper: Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, and Tat-Seng Chua, Adversarial Training Towards Robust Multimedia Recommender System.

阿里云MNS

Feature Scattering Adversarial Training

Code implementation of the paper "With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning", at USENIX Security 2018

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记