Open Access
Review
Table 2
Representative adversarial attack methods
Attack method | Year | Venue | Adv. target | Adv. knowledge | Perturb. structure | Adv. bounds | Applicable DNNs | Description |
---|---|---|---|---|---|---|---|---|
FGSM[4] | 2015 | ICLR | Untargeted | White-box | Noise-based |
![]() |
CNN | Fast gradient sign method |
BIM[7] | 2017 | ICLR | Untargeted | White-box | Noise-based |
![]() |
Inception-v3 | Iterative FGSM |
JSMA[34] | 2016 | EuroS&P | Targeted | White-box | Noise-based |
![]() |
LeNet-5 |
![]() |
DeepFool[38] | 2016 | CVPR | Untargeted | White-box | Noise-based |
![]() |
CaffeNet and GoogLeNet | Boundary distance estimation |
C&W[5] | 2017 | S&P | Untargeted | White-box | Noise-based |
![]() |
Inception-v3 | Minimal perturbations within ![]() |
UAP[39] | 2017 | CVPR | Untargeted | White-box | Noise-based |
![]() |
CaffeNet, VGG, GoogLeNet and ResNet-50 | Universal adversarial perturbation |
AdvPatch[30] | 2017 | ArXiv | Targeted | White-box | Patch-based |
![]() |
Inception-v3, ResNet-50, VGG-16 and VGG-19 | Adversarial patch to fool image classification |
Boundary[40] | 2017 | ICLR | Both | Black-box | Noise-based |
![]() |
VGG-19, ResNet-50 |
Query-based random walk attack |
PGD[6] | 2018 | ICLR | Untargeted | White-box | Noise-based |
![]() |
CNN, ResNet | Projected gradient descent |
stAdv[29] | 2018 | ICLR | Targeted | White-box | Transformation | Perceptual | CNN, ResNet-32 and ResNet-34 | Perturbation by flow field |
AC-GAN[37] | 2018 | NeurIPS | Both | White-box | Semantic | Unrestricted | ResNet | Unrestricted attacks from scratch using GANs |
RP2[24] | 2018 | CVPR | Targeted | White-box | Patch-based | Unrestricted | Inception-v3, LISA-CNN and GTSRB-CNN |
Physical adversarial patches in the real world |
SparseFool[41] | 2019 | CVPR | Untargeted | White-box | Noise-based |
![]() |
LeNet, VGG-19 and ResNet-18 | Projected ![]() |
AdvSticker[31] | 2019 | ICML | Targeted | White-box | Patch-based | Unrestricted | ResNet-50 | Adversarial camera stickers |
DIM[42] | 2019 | CVPR | Targeted | Black-box | Noise-based |
![]() |
Inception-v3, Inception-v4, ResNetv2-152 and InceptionResNet-v2 | Diverse input method |
TIM[43] | 2019 | CVPR | Untargeted | White-box | Transformation |
![]() |
Inception-v3, Inception-v4, Inception ResNet-v2 and ResNetv2-152 | Translation-invariant method |
SIM[44] | 2020 | ICLR | Targeted | White-box | Noise-based |
![]() |
Incv3ens3,Incv3ens4 and IncRes-v2ens | Scale-invariant method |
One-shot[45] | 2020 | CVPR | Both | White-box | Noise-based |
![]() |
ResNet-50, MobileNet-v2 | Optimizing objectives using dual attention mechanism |
PerC-AL[28] | 2020 | CVPR | Untargeted | White-box | Transformation | Perceptual | Inception-v3 | Bounded by perceptual color distance |
Square[27] | 2020 | ECCV | Both | Black-box | Noise-based |
![]() |
Inception-v3, ResNet-50 and VGG-16-BN | Query-based attack using shrinking squares |
AutoAttack[46] | 2020 | ICML | Both | White-/Black-box | Noise-based |
![]() |
General | Ensemble of APGD, Square and FAB attack |
SemAdv[47] | 2020 | ECCV | Targeted | White-/Black-box | Semantic | Unrestricted | ResNet-50, ResNet-101 |
Modifying visual attribute |
Rays[48] | 2020 | ICM | Both | Black-box | Noise-based |
![]() |
Transformers | Hard label attack for transformer |
Adaptive black-box attack[49] | 2020 | ArXiv | Both | Black-box | Noise-based |
![]() |
Transformers | Attacking trained synthetic models |
AOA[50] | 2020 | ArXiv | Targeted | White-box | Semantic | Unrestricted | VGG-19, ResNet-50, DenseNet-121 and Inception-v3 | Attack on attention |
ATA[51] | 2020 | CVPR | Untargeted | White-/Black-box | Noise-based | Unrestricted | ResNet-v2, Inception-v3, Inception-v4 and Inception-ResNet-v2 | Attention-guided transfer attack |
SAGA[52] | 2021 | ICCV | Both | White-box | Noise-based |
![]() |
Transformers | Attack self-attention mechanism of the visual transformer |
FilterFool[53] | 2021 | TIP | Untargeted | White-box | Semantic | Perceptual | ResNet-50, ResNet-18 and Alex |
Attack with mimic filter |
TTA[54] | 2021 | NeurIPS | Targeted | Black-box | Noise-based |
![]() |
ResNet-50, Dense-121, VGG-16 and Inception-v3 | Simple targeted transfer-based attack |
SparseGAN[33] | 2022 | CVPR | Untargeted | White-box | Semantic |
![]() |
Inception-v3, ResNet-50, VGG-16 and Densenet-161 |
Perturbation decoupling |
AMT-GAN[55] | 2022 | CVPR | Untargeted | White-box | Semantic | Unrestricted | IR-152, IRSE-50, Facenet and Mobileface | Make-up transfer attack |
NAA[56] | 2022 | CVPR | Untargeted | Black-box | Noise-based |
![]() |
Inception-v3, Inception-v4, Inception-ResNet-v2 and ResNetv2-152 | Neuron Attribution-based attack |
NP-Attack[57] | 2023 | PR | Both | Black-box | Noise-based |
![]() |
Inception-v3 | Query-based attack with tiling tricks |
ACA[58] | 2023 | NeurIPS | Untargeted | White-box | Semantic | Unrestricted | CNN, ViT | Content-based unrestricted adversarial attack |
DAS[59] | 2023 | CVPR | Untargeted | White-box | Noise-based |
![]() |
Inception-v3, VGG-19, ResNet-152, DenseNet, Yolo-V5, SSD, Faster R-CNN, and Mask R-CNN |
Generating adversarial examples by suppressing model and human attention |
MMIA[60] | 2024 | CVPR | Untargeted | White-box | Noise-based |
![]() |
ResNet-50, VGG-16, Inception-v3, DenseNet, MobileNet and GoogleNet |
Improving the stealthiness of adversarial examples |
AAS-AT[61] | 2024 | CVPR | Both | White-box | Transformation | Unrestricted | ViT | Adaptive attention scaling attack |
SASD-WS[62] | 2024 | CVPR | Targeted | Black-box | Noise-based |
![]() |
General | Attacks with sharpness-aware self-distillation and weight scaling |
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.