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The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating, defending, and certifying machine learning models against adversarial threats. It wraps models from any framework behind a common estimator interface, enabling composable pipelines for attack generation, defense application, robustness certification, and privacy auditing across evasion, poisoning, and extraction threats. The library distinguishes itself by covering the full adversarial ML security lifecycle within a single toolkit. It supports gradient-based adversarial example
Train GTSRB dataset ` python main.py --dataset GTSRB --numclass 43 --a 0.3 --b 0.1 --weightdecay 0 Train CelebA dataset python main.py --dataset CelebA --numclass 8 --a 0.3 --b 0.1 --weightdecay 1e-4 `
Implementation of Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning paper
We propose a noval global backdoor trigger called Procedural Noise Trigger, which is generated by the procedural noise.
The main features of 928082786/pnoiseattack are: Poisoning Attack Implementations.
Projects with overlapping indexed features include: trusted-ai/adversarial-robustness-toolbox — The Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating,… ekko-zn/ijcai2022-backdoor — Train GTSRB dataset `` python main.py --dataset GTSRB --numclass 43 --a 0.3 --b 0.1 --weightdecay 0 ` Train CelebA… georgepisl/backdoor-attacks-based-on-deep-learning — Implementation of Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning paper. hsouri/sleeper-agent — This code is the official PyTroch implementation of the Sleeper Agent. Our implementation is based on Industrial Scale… madrylab/label-consistent-backdoor-code — This repository contains the code to replicate experiments in our paper:. dreamtalecore/refool — Our paper is accepted by ECCV 2020.
