# SAE: Sustainable Adversarial Example Evaluation Framework for Class-Incremental Learning

## 🌟 Overview

_**News**: This work has been accepted as a poster by [AAAI 2026](https://aaai.org/conference/aaai/aaai-26/)._

**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](https://github.com/LAMDA-CL/PyCIL) 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`.
  * **Pre-trained CLIP** is downloaded from [HuggingFace](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K).
* `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.
* `models/`: Contains implementations of **9** CIL algorithms: **BiC**, **DER**, **Finetune**, **Foster**, **iCaRL**, **MEMO**, **PodNet**, **Replay**, **WA**.
* `scripts/`: Shell scripts for CIL training and attack benchmarking.
* `utils/`: Utility functions for augmentation, logging, dataset processing, visualization, etc.
* `attack.py`: Main entry point to run adversarial attacks.
* `trainCIL.py`: Main entry point to train CIL models.

### Dependency Requirements

* **OS**: Ubuntu 22.04
* **Python**: 3.12
* **PyTorch**: ≥ 2.1
* **GPU**: NVIDIA RTX 4090 (24GB VRAM)

To set up the environment:

```bash
conda env create -f environment.yml
conda activate SAE
```

---

## 🚀 Result Reproduction

### Step 1: CIL Training

First, you need to train the target CIL models follow the instruction below.

Example: training a single CIL method (e.g., **iCaRL**) on **CIFAR-100**:

```bash
python trainCIL.py --config exps/icarl.json
```

Example: training a single CIL method (e.g., **iCaRL**) on **ImageNet-100**:

```bash
python trainCIL.py --config exps/icarl-imagenet100.json
```

We also provide scripts to train all 9 CIL methods on **CIFAR-100**:

```bash
./scripts/trainCIL-CIFAR100.sh
```

Train all 9 CIL methods on **ImageNet-100**:

```bash
./scripts/trainCIL-ImageNet100.sh
```

All model checkpoints will be saved under the `logs/` directory, organized by method and dataset.


---

### Step 2: Adversarial Attack Benchmarking

_Note: assume that you have already prepared the dataset, the CIL model, and the CLIP model._

To launch an **SAE** attack targeting class **0** on a CIL model trained on **CIFAR-100**:

```bash
python attack.py --config exps/icarl.json --attack_method SAE --target_class 0
```

To test a different attack baseline, simply change the `--attack_method` argument.

You can reproduce the overall evaluation results by runing the below scripts (referring to the **Table 1** in our paper).

```bash
./scripts/attacks-CIFAR100.sh
./scripts/attacks-ImageNet100.sh
```

#### Benchmark Results

All benchmark results are available in the `appendix.pdf` of Supplementary Material.