Video Backdoor Attack
Clean-Label Backdoor Attacks on Video Recognition Models, CVPR2020
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Adversarial Face Attack | 338 | 3 years ago | gpl-3.0 | Python | ||||||
| Black-Box Adversarial Attack on Public Face Recognition Systems | ||||||||||
| Adversarial.js | 120 | 2 years ago | 2 | JavaScript | ||||||
| Break neural networks in your browser 🦹♂️ | ||||||||||
| Facerecognizer | 29 | 7 years ago | Python | |||||||
| :boy: A face recognition algorithm which notifies you on facebook in case of an Intruder attack | ||||||||||
| Gard Adversarial Speaker Id | 22 | 3 years ago | 1 | Python | ||||||
| Adversarial attack and defense strategies for deep speaker recognition systems | ||||||||||
| Deep Co Training For Semi Supervised Image Recognition | 22 | 5 years ago | Python | |||||||
| Unofficial implementation of the paper 'Deep Co-Training for Semi-Supervised Image Recognition' | ||||||||||
| Accessorize To A Crime | 21 | 5 years ago | MATLAB | |||||||
| Code for attacking state-of-the-art face-recognition system from our paper: M. Sharif, S. Bhagavatula, L. Bauer, M. Reiter. "Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition." In Proc. CCS, 2016. | ||||||||||
| Translearn | 14 | 5 years ago | 1 | mit | Python | |||||
| Code implementation of the paper "With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning", at USENIX Security 2018 | ||||||||||
| Attack_face_recognition | 11 | 4 years ago | 1 | Python | ||||||
| The implementation of AttackAI in Ali Tianchi Security Competition Phase One. | ||||||||||
| Video Backdoor Attack | 10 | 4 years ago | apache-2.0 | Python | ||||||
| Clean-Label Backdoor Attacks on Video Recognition Models, CVPR2020 | ||||||||||
| Ensemble_adversarial_attack | 10 | 4 years ago | Python | |||||||
| Ensemble Adversarial Black-Box Attacks against Deep Learning Systems Trained by MNIST, USPS and GTSRB Datasets | ||||||||||
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