作者: DAUNT 時間: 2025-3-21 20:37 作者: consolidate 時間: 2025-3-22 03:51
,AdvDO: Realistic Adversarial Attacks for?Trajectory Prediction,her prediction accuracy, few study the adversarial robustness of their methods. To bridge this gap, we propose to study the adversarial robustness of data-driven trajectory prediction systems. We devise an optimization-based adversarial attack framework that leverages a carefully-designed . to gener作者: 現(xiàn)實 時間: 2025-3-22 06:46 作者: Arteriography 時間: 2025-3-22 09:05
One Size Does NOT Fit All: Data-Adaptive Adversarial Training,ne of the most effective ways to improve the model’s adversarial robustness, it usually yields models with lower natural accuracy. In this paper, we argue that, for the attackable examples, traditional adversarial training which utilizes a fixed size perturbation ball can create adversarial examples作者: FOLD 時間: 2025-3-22 16:45
,UniCR: Universally Approximated Certified Robustness via?Randomized Smoothing,ximated certified robustness (UniCR) framework, which can approximate the robustness certification of . input on . classifier against . . perturbations with noise generated by . continuous probability distribution. Compared with the state-of-the-art certified defenses, UniCR provides many significan作者: FOLD 時間: 2025-3-22 19:24 作者: archetype 時間: 2025-3-22 23:58
,Robust Network Architecture Search via?Feature Distortion Restraining,domains. Most of existing methods improve model robustness from weight optimization, such as adversarial training. However, the architecture of DNNs is also a key factor to robustness, which is often neglected or underestimated. We propose Robust Network Architecture Search (RNAS) to obtain a robust作者: 歡笑 時間: 2025-3-23 04:12
,SecretGen: Privacy Recovery on?Pre-trained Models via?Distribution Discrimination,ned models are released online to facilitate further research. However, it raises extensive concerns on whether these pre-trained models would leak privacy-sensitive information of their training data. Thus, in this work, we aim to answer the following questions: “Can we effectively recover private 作者: BUDGE 時間: 2025-3-23 07:41 作者: 有害 時間: 2025-3-23 11:31 作者: 盤旋 時間: 2025-3-23 15:33 作者: 捐助 時間: 2025-3-23 21:23
,Learning Energy-Based Models with?Adversarial Training,nergy function that models the support of the data distribution, and the learning process is closely related to MCMC-based maximum likelihood learning of EBMs. We further propose improved techniques for generative modeling with AT, and demonstrate that this new approach is capable of generating dive作者: 剝皮 時間: 2025-3-24 00:00
,Adversarial Label Poisoning Attack on?Graph Neural Networks via?Label Propagation,ever, labeling graph data for training is a challenging task, and inaccurate labels may mislead the training process to erroneous GNN models for node classification. In this paper, we consider label poisoning attacks on training data, where the labels of input data are modified by an adversary befor作者: airborne 時間: 2025-3-24 03:51
,Revisiting Outer Optimization in?Adversarial Training, optimization. This paper aims to analyze this choice by investigating the overlooked role of outer optimization in AT. Our exploratory evaluations reveal that AT induces higher gradient norm and variance compared to NT. This phenomenon hinders the outer optimization in AT since the convergence rate作者: theta-waves 時間: 2025-3-24 08:52 作者: atrophy 時間: 2025-3-24 13:36 作者: 蘆筍 時間: 2025-3-24 16:44 作者: Debility 時間: 2025-3-24 21:59 作者: 大門在匯總 時間: 2025-3-25 00:21 作者: Analogy 時間: 2025-3-25 06:29
Richard Simpson,Monika Zimmermannost effective combination of image transformations specific to the input image. Extensive experiments on ImageNet demonstrate that our method significantly improves the attack success rates on both normally trained models and defense models under various settings.作者: Sigmoidoscopy 時間: 2025-3-25 07:46 作者: licence 時間: 2025-3-25 13:23
,Adaptive Image Transformations for?Transfer-Based Adversarial Attack,ost effective combination of image transformations specific to the input image. Extensive experiments on ImageNet demonstrate that our method significantly improves the attack success rates on both normally trained models and defense models under various settings.作者: AND 時間: 2025-3-25 17:58
,AdvDO: Realistic Adversarial Attacks for?Trajectory Prediction,ad an AV to drive off road or collide into other vehicles in simulation. Finally, we demonstrate how to mitigate the adversarial attacks using an adversarial training scheme (Our project website is at .).作者: 連詞 時間: 2025-3-25 23:27
Conference proceedings 2022ing; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation..作者: evasive 時間: 2025-3-26 03:26
0302-9743 puter Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022..?The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforce作者: Infiltrate 時間: 2025-3-26 07:05 作者: 死亡 時間: 2025-3-26 09:26
Richard Simpson,Monika Zimmermannhe number of alpha maps can be dynamically adjusted and can differ between training and inference, alleviating memory concerns and enabling fast training of GMPIs in less than half a day at a resolution of .. Our findings are consistent across three challenging and common high-resolution datasets, including FFHQ, AFHQv2?and MetFaces.作者: 頑固 時間: 2025-3-26 13:28
,Industry and Trade, 1800–1938,-by-case analysis, (3) tightness validation of certified robustness, and (4) optimality validation of noise distributions used by randomized smoothing. We conduct extensive experiments to validate the above benefits of UniCR and the advantages of UniCR over state-of-the-art certified defenses against . perturbations.作者: AXIOM 時間: 2025-3-26 20:22 作者: Femine 時間: 2025-3-26 22:15 作者: 鋼筆尖 時間: 2025-3-27 04:20
,UniCR: Universally Approximated Certified Robustness via?Randomized Smoothing,-by-case analysis, (3) tightness validation of certified robustness, and (4) optimality validation of noise distributions used by randomized smoothing. We conduct extensive experiments to validate the above benefits of UniCR and the advantages of UniCR over state-of-the-art certified defenses against . perturbations.作者: 一個攪動不安 時間: 2025-3-27 09:02
,Learning Energy-Based Models with?Adversarial Training,l-suited for image translation tasks, and exhibits strong out-of-distribution adversarial robustness. Our results demonstrate the viability of the AT approach to generative modeling, suggesting that AT is a competitive alternative approach to learning EBMs.作者: BET 時間: 2025-3-27 09:49
https://doi.org/10.1007/978-3-031-20065-6Computer Science; Informatics; Conference Proceedings; Research; Applications作者: Explosive 時間: 2025-3-27 16:28 作者: Stress 時間: 2025-3-27 19:34 作者: 多余 時間: 2025-3-28 01:17
Richard Simpson,Monika Zimmermannlizes several image transformation operations to improve the transferability of adversarial examples, which is effective, but fails to take the specific characteristic of the input image into consideration. In this work, we propose a novel architecture, called Adaptive Image Transformation Learner (作者: lipoatrophy 時間: 2025-3-28 04:42
Richard Simpson,Monika ZimmermannWe find that only two modifications are absolutely necessary: 1) a multiplane image style generator branch which produces a set of alpha maps conditioned on their depth; 2) a pose-conditioned discriminator. We refer to the generated output as a ‘generative multiplane image’ (GMPI) and emphasize that作者: STELL 時間: 2025-3-28 10:00
Kuwait and World Oil Developments,her prediction accuracy, few study the adversarial robustness of their methods. To bridge this gap, we propose to study the adversarial robustness of data-driven trajectory prediction systems. We devise an optimization-based adversarial attack framework that leverages a carefully-designed . to gener作者: 儀式 時間: 2025-3-28 13:58 作者: 嚴厲譴責 時間: 2025-3-28 16:44 作者: inchoate 時間: 2025-3-28 22:21
,Industry and Trade, 1800–1938,ximated certified robustness (UniCR) framework, which can approximate the robustness certification of . input on . classifier against . . perturbations with noise generated by . continuous probability distribution. Compared with the state-of-the-art certified defenses, UniCR provides many significan作者: 光明正大 時間: 2025-3-28 23:17 作者: pineal-gland 時間: 2025-3-29 04:25
The Sixteenth-Century Growth of the Marketdomains. Most of existing methods improve model robustness from weight optimization, such as adversarial training. However, the architecture of DNNs is also a key factor to robustness, which is often neglected or underestimated. We propose Robust Network Architecture Search (RNAS) to obtain a robust作者: Diuretic 時間: 2025-3-29 10:19 作者: 樂意 時間: 2025-3-29 13:46
Disputes and Levels of Litigationdiction label. Great efforts have been made recently to decrease the number of queries; however, existing decision-based attacks still require thousands of queries in order to generate good quality adversarial examples. In this work, we find that a benign sample, the current and the next adversarial作者: Stable-Angina 時間: 2025-3-29 17:55 作者: 令人作嘔 時間: 2025-3-29 22:56
Disputes and Levels of Litigational hard-label setting, we observe that existing methods suffer from catastrophic performance degradation. We argue this is due to the lack of rich information in the probability prediction and the overfitting caused by hard labels. To this end, we propose a novel hard-label model stealing method ter作者: 和諧 時間: 2025-3-30 00:12 作者: GUEER 時間: 2025-3-30 04:22 作者: overhaul 時間: 2025-3-30 09:21 作者: 憤慨一下 時間: 2025-3-30 13:18 作者: 牽連 時間: 2025-3-30 18:00
Palgrave Studies in Economic Historyingle input-agnostic trigger and targeting only one class to using multiple, input-specific triggers and targeting multiple classes. However, Trojan defenses have not caught up with this development. Most defense methods still make inadequate assumptions about Trojan triggers and target classes, thu作者: MOAN 時間: 2025-3-31 00:13
Defining the Region: Geography and History,algorithms aim at defending attacks constrained within low magnitude Lp norm bounds, real-world adversaries are not limited by such constraints. In this work, we aim to achieve adversarial robustness within larger bounds, against perturbations that may be perceptible, but do not change human (or Ora作者: endocardium 時間: 2025-3-31 02:17
https://doi.org/10.1007/978-3-030-72900-4function. Since the queries contain very limited information about the loss, black-box methods usually require much more queries than white-box methods. We propose to improve the query efficiency of black-box methods by exploiting the smoothness of the local loss landscape. However, many adversarial作者: Demonstrate 時間: 2025-3-31 07:50
Computer Vision – ECCV 2022978-3-031-20065-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 削減 時間: 2025-3-31 12:32 作者: 哭得清醒了 時間: 2025-3-31 16:49
https://doi.org/10.1057/978-1-137-56598-3ng strategy empowers the DAAT models with impressive robustness while retaining remarkable natural accuracy. Based on a toy example, we theoretically prove the recession of the natural accuracy caused by adversarial training and show how the data-adaptive perturbation size helps the model resist it.作者: Chemotherapy 時間: 2025-3-31 18:40 作者: 厚顏 時間: 2025-3-31 23:21 作者: patella 時間: 2025-4-1 02:29 作者: 摸索 時間: 2025-4-1 08:53
