Megum1/LOTUS
[CVPR'24] LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning
This project helps AI security researchers and red teamers understand and demonstrate a new type of backdoor attack on image classification models. It takes a dataset of images (like CIFAR-10) and a pre-trained model, then applies 'backdoor' triggers that cause the model to misclassify specific inputs to a target class. The output is a modified model that behaves normally on most inputs but exhibits the targeted misclassification when activated by the hidden trigger.
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Use this if you are a security researcher or red teamer studying adversarial attacks and want to implement and test a state-of-the-art evasive backdoor technique on image classification models.
Not ideal if you are looking to defend against backdoor attacks or need a tool for general image classification tasks without exploring model vulnerabilities.
Stars
15
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Language
Python
License
MIT
Category
Last pushed
Jan 15, 2025
Commits (30d)
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