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1. QAA QAA Public

   This is the official code repository for paper "Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization"

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2. CodeTAE CodeTAE Public

   This is the official code repository for paper "Exploiting the Adversarial Example Vulnerability of Transfer Learning of Source Code".

   Python 6

3. VT-PyTorch VT-PyTorch Public

   The PyTorch implementation (non-official) of paper "Enhancing the Transferability of Adversarial Attacks through Variance Tuning"

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4. AugMax AugMax Public

   Forked from VITA-Group/AugMax

   [NeurIPS'21] "AugMax: Adversarial Composition of Random Augmentations for Robust Training" by Haotao Wang, Chaowei Xiao, Jean Kossaifi, Zhiding Yu, Animashree Anandkumar, and Zhangyang Wang.

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