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Environment: Python3.6.5, TensorFlow-gpu1.14 Dataset: UCF101, Model: I3D This is our paper link. You can firstly run traincleanmodel.py to get a clean-trained I3D model. The generatetrigger.py and enhancetrigger.py correspond to Backdoor Trigger Generation and Enhancing Backdoor Trigger sections…
The main features of shihaozhaozsh/video-backdoor-attack 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,… dreamtalecore/refool — Our paper is accepted by ECCV 2020. 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… advdoor/advdoor.
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 `
