🔥[2024-07-24] Papers of ICML 2024 have been updated here!
🔥[2024-07-04] Papers of CVPR 2024 have been updated here!
| Title | Publish | Repo | Paper | Summary |
|---|---|---|---|---|
| Content-based Unrestricted Adversarial Attack | NeurIPS | - | summary | |
| Diff-PGD: Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability | NeurIPS | summary | ||
| Downstream-agnostic Adversarial Examples | ICCV | |||
| AdvDiffuser: Natural Adversarial Example Synthesis with Diffusion Models | ICCV | summary | ||
| Frequency-aware GAN for Adversarial Manipulation Generation | ICCV | - | ||
| AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Models | - | summary | ||
| Diffusion Models for Imperceptible and Transferable Adversarial Attack | - | |||
| Improving Adversarial Transferability by Stable Diffusion | - | - | ||
| Semantic Adversarial Attacks via Diffusion Models | BMVC | summary |
| Title | Publish | Repo | Paper | Summary |
|---|---|---|---|---|
| Towards Feature Space Adversarial Attack | ** | summary |
| Title | Publish | Repo | Paper | Summary |
|---|---|---|---|---|
| Unrestricted Adversarial Examples via Semantic Manipulation | ICLR | summary | ||
| SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing | ECCV | summary | ||
| Colorfool: Semantic adversarial colorization | CVPR | - | - | - |
| Title | Publish | Repo | Paper | Summary |
|---|---|---|---|---|
| Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers | ICCV | summary | ||
| Rob-GAN: Generator, Discriminator, and Adversarial Attacker | CVPR | summary | ||
| ADef: an Iterative Algorithm to Construct Adversarial Deformations | ICLR | - | - | - |
| AdvGAN++: Harnessing Latent Layers for Adversary Generation | CVPRW | summary | ||
| One pixel attack for fooling deep neural networks | IEEE TEVC | - | - | - |
| Title | Publish | Repo | Paper | Summary |
|---|---|---|---|---|
| Intriguing Properties of Neural Networks. | ICLR 2014 | - | summary | |
| FGSM: Explaining and Harnessing Adversarial Examples | ICLR 2015 | - | summary | |
| Deepfool: a simple and accurate method to fool deep neural networks | CVPR 2016 | - | ||
| Universal adversarial perturbations | CVPR 2017 | - | ||
| Towards evaluating the robustness of neural networks | 2017 IEEE Symposium on Security and Privacy (SP) | - | - | - |
| Ensemble Adversarial Training: Attacks and Defenses | ICLR 2018 | - | - | |
| PGD: Towards Deep Learning Models Resistant to Adversarial Attacks | ICLR 2018 | - | ||
| Generating Natural Adversarial Examples | ICLR 2018 | summary | ||
| Constructing Unrestricted Adversarial Examples with Generative Models | NeurIPS 2018 | summary | ||
| NAG: Network for Adversary Generation | CVPR 2018 | summary | ||
| Semantic Adversarial Examples | CVPRW 2018 | summary | ||
| AdvGAN: Generating adversarial examples with adversarial networks | IJCAI 2018 | summary | ||
| ATN: Learning to Attack: Adversarial Transformation Networks | AAAI 2018 | summary |