Towards Transferable Targeted 3D Adversarial Attack in the Physical World |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
3D, physical attack |
VA3: Virtually Assured Amplification Attack on Probabilistic Copyright Protection for Text-to-Image Generative Models |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Attack To Defend: Exploiting Adversarial Attacks for Detecting Poisoned Models |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
Poisoning/Trojan/Backdoor attack defense |
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
PAD: Patch-Agnostic Defense against Adversarial Patch Attacks |
CVPR |
![Github](https://camo.githubusercontent.com/f3ddbdd75ddfb25a893bcfc86d5ce2536523434eede4df79899e27dd2566cf69/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4c696875612d4a696e672f504144) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning |
CVPR |
![Github](https://camo.githubusercontent.com/b19e64fc801a2830aa6278da7550cd179b243f3be1a27fe44038420456b24e24/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4d6567756d312f4c4f545553) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Deep-TROJ: An Inference Stage Trojan Insertion Algorithm through Efficient Weight Replacement Attack |
CVPR |
![Github](https://camo.githubusercontent.com/9249d23b8fc8948f68c0fc84a0c74268381ffc3aaa970081cc3e9aed143ebeb4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4d4c2d53656375726974792d52657365617263682d4c41422f446565702d54524f4a) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Physical Backdoor: Towards Temperature-based Backdoor Attacks in the Physical World |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Re-thinking Data Availability Attacks Against Deep Neural Networks |
CVPR |
![Github](https://camo.githubusercontent.com/7ac9d332b2993b3771703be83332057412aff03115e41daf160469cb255a9c8b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f457574657270654b2f52657468696e6b696e672d446174612d417661696c6162696c6974792d41747461636b73) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Semantic-Aware Multi-Label Adversarial Attacks |
CVPR |
![Github](https://camo.githubusercontent.com/9ce40ecda33177c896cc90cb183180f73b311b2d05630659667df93e7f17a4d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f68617373616e2d6d61686d6f6f642f53656d616e7469634d4c4c41747461636b73) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Nearest is Not Dearest: Towards Practical Defense against Quantization-conditioned Backdoor Attacks |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Overload: Latency Attacks on Object Detection for Edge Devices |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Data Poisoning based Backdoor Attacks to Contrastive Learning |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Intriguing Properties of Diffusion Models: An Empirical Study of the Natural Attack Capability in Text-to-Image Generative Models |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
MMA-Diffusion: MultiModal Attack on Diffusion Models |
CVPR |
![Github](https://camo.githubusercontent.com/7f2550a674519679dbc01d9a6b187791f033bc37b87a606acc85d4b3592847f1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f637572652d6c61622f4d4d412d446966667573696f6e) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning |
CVPR |
![Github](https://camo.githubusercontent.com/ace54ba41404b9065f93deee20f5eccdf4f9a089a9864ca45be1ce100d3fdd30/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f434c494167726f75702f414e4441) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
Perturbation attack, Optimization, Transferability Enhancement |
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity Training |
CVPR |
![Github](https://camo.githubusercontent.com/cdacc86bd2273da9c8978450c9aa9e7d7773650c6d03833b2f362b71eb85026f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4d69737465725270656e672f4547532d54535341) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision Transformers |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Not All Prompts Are Secure: A Switchable Backdoor Attack Against Pre-trained Vision Transfomers |
CVPR |
![Github](https://camo.githubusercontent.com/8f78a0c7078e2eed6bb97abe8d5d025cddf286bb0976decfc94b988b4a2fdc89/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f554344766973696f6e2f536c6f77466f726d6572) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
GLOW: Global Layout Aware Attacks on Object Detection |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Improving Transferable Targeted Adversarial Attacks with Model Self-Enhancement |
CVPR |
![Github](https://camo.githubusercontent.com/646aee1aa5f420d718a55eebaf1ec4d6bea20d87fc0bd58fe3ab1a939d28573d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6734616c6c6c662f53415344) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
BrainWash: A Poisoning Attack to Forget in Continual Learning |
CVPR |
![Github](https://camo.githubusercontent.com/832ded6504a1f0baa802416ab5259bd14c589a6b3271283015a4da486b85c50b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6d696e742d76752f427261696e77617368) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
BadCLIP: Dual-Embedding Guided Backdoor Attack on Multimodal Contrastive Learning |
CVPR |
![Github](https://camo.githubusercontent.com/917d748d0b8effbd010f2149cc1b8984062c8b5a2dcb813bdb3e1ffb007ea129/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4c69616e6753697975616e32312f426164434c4950) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Towards Fairness-Aware Adversarial Learning |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point Clouds |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Structured Gradient-based Interpretations via Norm-Regularized Adversarial Training |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Dispel Darkness for Better Fusion: A Controllable Visual Enhancer based on Cross-modal Conditional Adversarial Learning |
CVPR |
![Github](https://camo.githubusercontent.com/83cc7ba3e9c9f5477ac6844a41ebff7db3da315475b08a55beea72b4b108ee49/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f48616f5a68616e67313031382f44444246) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
ASAM: Boosting Segment Anything Model with Adversarial Tuning |
CVPR |
![Github](https://camo.githubusercontent.com/e75550f1d6e2b63908b206be14d97aced6639e4b49e5a735837b43b7535e33dd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c75636b7962697264313939342f4153414d) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
NAPGuard: Towards Detecting Naturalistic Adversarial Patches |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Towards Understanding and Improving Adversarial Robustness of Vision Transformers |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Infrared Adversarial Car Stickers |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Boosting Adversarial Transferability by Block Shuffle and Rotation |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models |
CVPR |
![Github](https://camo.githubusercontent.com/6950fc9ad02793c3291a28d1e9bb041fcf8c5e66e10745084cbfcd37c12ee8d4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f547265654c4c692f415054) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Adversarial Score Distillation: When score distillation meets GAN |
CVPR |
![Github](https://camo.githubusercontent.com/8bd5e5aac74ae3c1cac3f367f49f0617d1868e65e5dac47e4927c5c1e0157b29/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f32793763332f415344) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Focus on Hiders: Exploring Hidden Threats for Enhancing Adversarial Training |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Robust Image Denoising through Adversarial Frequency Mixup |
CVPR |
![Github](https://camo.githubusercontent.com/a46783eff10903461f1c492a9633404d97069d8432469e8bbb42d36fdc7739be/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f646872796f756769742f41464d) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Towards Robust 3D Pose Transfer with Adversarial Learning |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
Robust Distillation via Untargeted and Targeted Intermediate Adversarial Samples |
CVPR |
- |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
- |
PeerAiD: Improving Adversarial Distillation from a Specialized Peer Tutor |
CVPR |
![Github](https://camo.githubusercontent.com/65e0eee20fe43750d4737d699fdb2f58f8e76d76b3e638c1591236052530ac44/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6a6165776f6e616c6976652f50656572416944) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Adversarial Distillation Based on Slack Matching and Attribution Region Alignment |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Adversarially Robust Few-shot Learning via Parameter Co-distillation of Similarity and Class Concept Learners |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Revisiting Adversarial Training at Scale |
CVPR |
![Github](https://camo.githubusercontent.com/bb20e9c9e4660937e0d6b5c25e89bf146a1a2e94a3ae97085b63e981cebb8c12/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f554353432d564c41412f416476584c) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Structure-Guided Adversarial Training of Diffusion Models |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Learning to Transform Dynamically for Better Adversarial Transferability |
CVPR |
![Github](https://camo.githubusercontent.com/44d5213f68979c2b4a45c4729492e19eb4ab075f6b42ec90b357d60893449b4d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f5a68616e67414950492f5472616e7366657241747461636b) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
Input transformation, Reinforcement learning |
Ensemble Diversity Facilitates Adversarial Transferability |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Revisiting Adversarial Training Under Long-Tailed Distributions |
CVPR |
![Github](https://camo.githubusercontent.com/3988b423a1bf4c8d08b3334f3087db39be46b256830b3edf326e9d0198264d36/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4e4953504c61622f41542d42534c) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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DAP: A Dynamic Adversarial Patch for Evading Person Detectors |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Pre-trained Model Guided Fine-Tuning for Zero-Shot Adversarial Robustness |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Revisiting Adversarial Training Under Long-Tailed Distributions |
CVPR |
![Github](https://camo.githubusercontent.com/3988b423a1bf4c8d08b3334f3087db39be46b256830b3edf326e9d0198264d36/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4e4953504c61622f41542d42534c) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Initialization Matters for Adversarial Transfer Learning |
CVPR |
![Github](https://camo.githubusercontent.com/4fab6e4652076440a0371482ce3ea1f095d1a4581ad653634348e517accfe157/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f446f6e67587a7a2f526f4c49) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Language-Driven Anchors for Zero-Shot Adversarial Robustness |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection |
CVPR |
![Github](https://camo.githubusercontent.com/252bcd61ebedbfba799666824e1744e831158dc248b8190e5b00a6623d77cb1c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f48616e7353756e592f44696666414d) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models |
CVPR |
![Github](https://camo.githubusercontent.com/66bbabd2b849227bfe44e9e7f942a5b73103401e3da9452038813e8bcbf588ca/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6b6f6e6731333636312f414354) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Adversarial Text to Continuous Image Generation |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM |
CVPR |
![Github](https://camo.githubusercontent.com/ac49e619c11e21ce4e4ad2cb63b617f6b25367114c12ab1a8cf4d802eb4b1f5b/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f746c7931382f54504150) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Boosting Adversarial Training via Fisher-Rao Norm-based Regularization |
CVPR |
![Github](https://camo.githubusercontent.com/ab25c63afe6f85f4ad493678d0adf41fc0bfe4017f24d62760b3482afbaf9fe6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f547275737441492f4c4f4154) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model |
CVPR |
![Github](https://camo.githubusercontent.com/b20c82d8421d1a429aff0462888914564e09906b93da43b991312c94886ab6d5/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f70736b79313131312f4d696d6963446966667573696f6e) |
![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Models |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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CAD: Photorealistic 3D Generation via Adversarial Distillation |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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Random Entangled Tokens for Adversarially Robust Vision Transformer |
CVPR |
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![cvpr](https://camo.githubusercontent.com/0c6665ea041106c110f23b61ac017b3136eece57ec83ca8a435197940467f208/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d373339354335) |
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