synthesizing-robust-adversarial-examplesMy entry for ICLR 2018 Reproducibility Challenge for paper Synthesizing robust adversarial examples https://openreview.net/pdf?id=BJDH5M-AW
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Mutual labels: adversarial-machine-learning, adversarial-example
EAD AttackEAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
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AdverseDriveAttacking Vision based Perception in End-to-end Autonomous Driving Models
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Mutual labels: adversarial-machine-learning
backdoors101Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.
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Mutual labels: adversarial-machine-learning
adversarial-code-generationSource code for the ICLR 2021 work "Generating Adversarial Computer Programs using Optimized Obfuscations"
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Mutual labels: adversarial-machine-learning
AMRThis 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.
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Mutual labels: adversarial-machine-learning
Adversarial-Patch-TrainingCode for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.
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Mutual labels: adversarial-machine-learning
jpeg-defenseSHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
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Adversarial Robustness ToolboxAdversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
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Mutual labels: adversarial-machine-learning
tulipScaleable input gradient regularization
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Mutual labels: adversarial-machine-learning
translearnCode implementation of the paper "With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning", at USENIX Security 2018
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Mutual labels: adversarial-machine-learning
advrankAdversarial Ranking Attack and Defense, ECCV, 2020.
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Mutual labels: adversarial-machine-learning
adversarial-recommender-systems-surveyThe 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-…
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Mutual labels: adversarial-machine-learning
robust-local-lipschitzA Closer Look at Accuracy vs. Robustness
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procedural-advmlTask-agnostic universal black-box attacks on computer vision neural network via procedural noise (CCS'19)
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athenaAthena: A Framework for Defending Machine Learning Systems Against Adversarial Attacks
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perceptron-benchmarkRobustness benchmark for DNN models.
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FawkesFawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes
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