├── .gitignore ├── .idea ├── adv_CVPR.iml ├── misc.xml └── modules.xml ├── README.md ├── cvpr_2017 ├── img │ ├── uap.png │ └── uap_table.png └── universal_adversarial_perturbations.md ├── cvpr_2018 ├── Boosting_adversarial_attacks_with_momentum.md ├── art_of_singular_vectors_and_universal_adversarial_perturbations.md ├── defense_against_adversarial_attacks_using_high_level_representation_guided_denoiser.md ├── defense_against_universal_adversarial_perturbations.md ├── deflecting_adversarial_attacks_with_pixel_deflection.md ├── generative_adversarial_perturbations.md ├── img │ ├── d_uap.png │ ├── d_uap_table.png │ ├── gap.png │ ├── gap_result.png │ ├── guided_denoiser.png │ ├── guided_denoiser_table.png │ ├── mifgsm.png │ ├── mifgsm_table.png │ ├── nag.png │ ├── nag_table.png │ ├── phy_attack.png │ ├── phy_attack_results.png │ ├── pixel_def.png │ ├── pixel_def_table.png │ ├── robust_segmentation.png │ ├── robust_segmentation_results.png │ ├── sv_uap.png │ └── sv_uap_table.png ├── nag_network_for_adversary_generation.md ├── on _the_robustness_of_semantic_segmentation_models_to_adversarial_attacks.md └── robust_physical_world_attacks_on_deep_learning_visual_classification.md ├── cvpr_2019 ├── adversarial_defense_by_stratified_convolutional_sparse_coding.md ├── adversarial_defense_through_network_profiling_based_path_extraction.md ├── aird_adversarial_learning_framework_for_image_repurposing_detection.md ├── barrage_of_random_transforms_for_adversarially_robust_defense.md ├── comdefend_an_efficient_image_compression_model_to_defend_adversarial_examples.md ├── defending_against_adversarial_attacks_by_randomized_diversification.md ├── defense_against_adversarial_images_using_web_scale_nearest_neighbor_search.md ├── detection_based_defense_agains_adversarial_examples_from_the_steganalysis_point_of_view.md ├── efficient_decision_based_black_box_adversarial_attacks_on_face_recognition.md ├── exact_adversarial_attack_to_image_captioning_via_structured_output_learning_with_latent_variables.md ├── feature_denoising_for_improving_adversarial_robustness.md ├── feature_distillation_dnn_oriented_jpeg_compression_against_adversarial_examples.md ├── fooling_automated_surveillance_cameras_adversarial_patches_to_attack_person_detection.md ├── img │ ├── aird.png │ ├── aird_results.png │ ├── captioning.png │ ├── captioning_table.png │ ├── comdefend.png │ ├── comdefend_table.png │ ├── face_recog.png │ ├── face_recog_table.png │ ├── feature_denoising_block.png │ ├── feature_denoising_table.png │ ├── feature_distillation.png │ ├── feature_distillation_table.png │ ├── img_retr.png │ ├── img_retr_table_1.png │ ├── img_retr_table_2.png │ ├── obj_det.png │ ├── obj_det_result.png │ ├── path_extraction_defense.png │ ├── path_extraction_defense_table.png │ ├── pni_table.png │ ├── pni_weights.png │ ├── rand_d.png │ ├── rand_t_results.png │ ├── steganalysis.png │ ├── steganalysis_table.png │ ├── strat_conv.png │ ├── strat_conv_table.png │ ├── web_search.png │ └── web_search_table.png ├── parametric_noise_injection_trainable_randomness_to_improve_deep_neural_network_robustness_against_adversarial_attack.md └── retrieval_augmented_convolutional_neural_networks_for_improve_robustness_against_adversarial_examples.md └── template.md /.gitignore: -------------------------------------------------------------------------------- 1 | .idea/* 2 | -------------------------------------------------------------------------------- /.idea/adv_CVPR.iml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BardOfCodes/adv_summaries/HEAD/.idea/adv_CVPR.iml -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BardOfCodes/adv_summaries/HEAD/.idea/misc.xml 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