SAE: Sustainable Adversarial Example Evaluation Framework for Class-Incremental Learning
🌟 Overview
News: This work has been accepted as a poster by AAAI 2026.
SAE (Sustainable Adversarial Example) is a universal adversarial attack framework targeting Class-Incremental Learning (CIL). This repository provides a comprehensive pipeline for both CIL training and benchmarking multiple attack methods, including our proposed SAE approach.
The project integrates with PyCIL: A Python Toolbox for Class-Incremental Learning for CIL model training. It also supports benchmarking several attack baselines alongside SAE, enabling fair and reproducible evaluations of adversarial robustness across CIL methods.
If you are interested in our work, please refer to:
@inproceedings{liu2026SAE,
title={Improving Sustainability of Adversarial Examples in Class-Incremental Learning},
author={Taifeng Liu, Xinjing Liu, Liangqiu Dong, Yang Liu, Yilong Yang, Zhuo Ma},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2026}
}
⚙️ Environment Setup
Project Layout
attacks/: Implementations of all attack baselines including MIFGSM, Gaker, AIM, CGNC, CleanSheet, UnivIntruder, and SAE.
convs/: Backbone definitions for CIL models and CLIP model, including resnet32, resnet50, cosine_resnet32, and cosine_resnet50.
datasets/: Dataset management for CIFAR-100 (32x32) and ImageNet-100 (224x224).
CIFAR-100 is automatically downloaded at runtime.
ImageNet-100 should be manually extracted from ImageNet-1K using create_imagenet100_from_imagenet.py, which parses train.txt and eval.txt to extract relevant classes.
exps/: JSON configuration files for various CIL training methods.
logs/: Stores trained CIL model checkpoints and evaluation results of attacks.
