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Table 3
Representative adversarial defense methods
| Defense method | Year | Venue | Applicable DNNs | Description |
|---|---|---|---|---|
| Adversarial training (AT) | ||||
| PGD-AT[6] | 2018 | ICLR | ResNet | Using PGD for AT |
| TRADES[8] | 2019 | ICML | CNN, ResNet-18 | Trade-off in AT between robustness and accuracy |
| MART[63] | 2019 | ICLR | ResNet-18 | Improving AT by revisiting misclassified samples |
| FAT[9] | 2020 | ICML | CNN, ResNet | Friendly AT with an early stopping mechanism |
| Fast AT[64] | 2020 | ICLR | ResNet-18, ResNet-50 | Using PGD for adversarial training |
| Patch-based negative augmentation[76] | 2022 | NeurIPS | ViT | Introducing negatively enhanced samples to train the model |
| LAS-AT[11] | 2022 | CVPR | ResNet-18, WideResNet-34-10 and PreActResNet-18 |
Adaptive AT with a strategy network |
| UIAT[77] | 2023 | CVPR | ResNet-18, PreActResNet-18 and WideResNet-28-10 | Improving AT with inverse adversarial examples |
| FP-Better[78] | 2024 | TIFS | ResNet18 | Fast propagation boosted AT |
| Robust network design | ||||
| FD[65] | 2019 | CVPR | ResNet-101, ResNet-152 | Feature denoising between network layers |
| ANP-VS[66] | 2020 | ICML | LeNet 5-Caffe, VGG-16 | Adversarial neural pruning |
| FSR[67] | 2023 | CVPR | ResNet-18, VGG-16 and WideResNet-34-10 | Feature separation and recalibration modules |
| PAAS[79] | 2024 | CVPR | ViT | Changing the attention mechanism within the network |
| Input transformation | ||||
| MagNet[80] | 2017 | SIGSAC | CNN | Preprocessing with denoising auto-encoders |
| Quilting[71] | 2018 | ICLR | ResNet-50 | Piecing together clean images using a database |
| TVM[71] | 2018 | ICLR | ResNet-50 | Total variance minimization |
| Defense-GAN[12] | 2018 | ICLR | CNN | Adversarial Purification (AP): GANs |
| ComDefend[81] | 2019 | CVPR | ComCNN + RecCNN | Bit depth compression and reconstruction |
| BuZz[49] | 2020 | ArXiv | ResNet-56，VGG-16 and ViT | Apply a unique transformation to the input image |
| A-VAE[82] | 2020 | ECCV | ResNet-50, VGGFace2 | AP: combining VAEs and GANs |
| MCMC-EBM[72] | 2021 | ICLR | ResNet-50 | AP: Monte-Carlo Markov-Chain energy-based model |
| DiffPure[10] | 2022 | ICML | ResNet-50, WideResNet-50-2 and ViT | AP: deep diffusion probabilistic models |
| DISCO[83] | 2022 | NeurIPS | ResNet-18 and WideResNet-28 | AP: super-resolution algorithms |
| Certified defenses | ||||
| Safety verification[68] | 2017 | CAV | CNN | Verification with satisfiability modulo theory |
| COAP[84] | 2018 | ICML | CNN | Defense with convex outer adversarial polytope |
| IBP[73] | 2018 | NeurIPS | CNN | Defense with interval bound propagation |
| Lipschitz continuity[69] | 2019 | NeurIPS | CNN | Use Lipschitz constant to bound the change in the output |
| MILP[75] | 2019 | ICLR | CNN | Mixed integer linear programming for robustness evalaution |
| PCT[85] | 2020 | ICLR | U-Net | Certified robustness against adversarial patches |
| Certifiable patch defense[86] | 2022 | CVPR | ResNet-50, ResNext-10 and ViT-S/16-224 | Practical verifiable patch defense |
| Ensemble defenses | ||||
| EAT[70] | 2018 | ICLR | InceptionResNet-v2 | AT with an ensemble of models |
| Diversity[87] | 2019 | ICML | ResNet-20 | Improving model diversity for ensemble defense |
| P&D[88] | 2021 | AAAI | CNN, ResNet | Improving model diversity and reducing transferability |
| SEAT[89] | 2022 | ICLR | ResNet-18 and WideResNet-32-10 | Self-ensemble AT using history models |
| iGAT[90] | 2023 | NeurIPS | ResNet-20 | A theory of ensemble defenses and improved methods |
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