xtransfer_naive_linf_eps12_non_targeted | 1 | X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP | ICML 2025 |
xtransfer_base_linf_eps12_non_targeted | 16 | X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP | ICML 2025 |
xtransfer_mid_linf_eps12_non_targeted | 32 | X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP | ICML 2025 |
xtransfer_large_linf_eps12_non_targeted | 64 | X-Transfer Attacks: Towards Super Transferable Adversarial Attacks on CLIP | ICML 2025 |
cpgc_clip_vit_b16_flicker30k | 1 | One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models | arXiv:2406.05491 |
cpgc_clip_vit_b16_mscoco | 1 | One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models | arXiv:2406.05491 |
cpgc_clip_rn101_flicker30k | 1 | One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models | arXiv:2406.05491 |
cpgc_clip_rn101_mscoco | 1 | One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Models | arXiv:2406.05491 |
etu_clip_rn50_flickr30_uap | 1 | Universal Adversarial Perturbations for Vision-Language Pre-trained Models | ACM SIGIR 2024 |
etu_clip_vit_b16_flickr30_uap | 1 | Universal Adversarial Perturbations for Vision-Language Pre-trained Models | ACM SIGIR 2024 |
metauap_normalized_logits_ensemble_coco | 1 | Learning transferable targeted universal adversarial perturbations by sequential meta-learning | Computers & Security 2024 |
metauap_normalized_logits_ensemble_coco_meta | 1 | Learning transferable targeted universal adversarial perturbations by sequential meta-learning | Computers & Security 2024 |
trmuap_googlenet | 1 | TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization | ICCV 2023 |
trmuap_resnet152 | 1 | TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization | ICCV 2023 |
gd_uap_dl_resnet_msc_with_all_data | 1 | Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations | TPAMI 2018 |
gd_uap_resnet_with_data | 1 | Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations | TPAMI 2018 |
darksam_point_ade_sam_vit_b_eps10 | 1 | Darksam: Fooling segment anything model to segment nothing | NeurIPS 2024 |
darksam_box_ade_sam_vit_b_eps10 | 1 | Darksam: Fooling segment anything model to segment nothing | NeurIPS 2024 |
darksam_point_sa1b_sam_vit_b_eps10 | 1 | Darksam: Fooling segment anything model to segment nothing | NeurIPS 2024 |
darksam_box_sa1b_sam_vit_b_eps10 | 1 | Darksam: Fooling segment anything model to segment nothing | NeurIPS 2024 |
darksam_point_coco_sam_vit_b_eps10 | 1 | Darksam: Fooling segment anything model to segment nothing | NeurIPS 2024 |
darksam_box_coco_sam_vit_b_eps10 | 1 | Darksam: Fooling segment anything model to segment nothing | NeurIPS 2024 |
darksam_point_city_sam_vit_b_eps10 | 1 | Darksam: Fooling segment anything model to segment nothing | NeurIPS 2024 |
darksam_box_city_sam_vit_b_eps10 | 1 | Darksam: Fooling segment anything model to segment nothing | NeurIPS 2024 |