作者: 憲法沒有 時間: 2025-3-21 20:34 作者: 放棄 時間: 2025-3-22 04:25
Deep ,-NN Defense Against Clean-Label Data Poisoning Attacks of . as well as for implementing the Deep .-NN defense on real-world datasets with class imbalance. Our proposed defense shows that current clean-label poisoning attack strategies can be annulled, and serves as a strong yet simple-to-implement baseline defense to test future clean-label poisoning a作者: 提名 時間: 2025-3-22 05:59 作者: frenzy 時間: 2025-3-22 11:37
Jacks of All Trades, Masters of None: Addressing Distributional Shift and Obtrusiveness via Transpar findings, we then focus on addressing how to overcome the principal limitations of scale for the deployment of attacks in real physical settings: namely the obtrusiveness of large patches. Our strategy is to turn to the novel design of irregularly-shaped, semi-transparent partial patches which we c作者: 信徒 時間: 2025-3-22 16:34 作者: 信徒 時間: 2025-3-22 19:27
Robust Super-Resolution of Real Faces Using Smooth Featuresdation GAN to convert bicubically downsampled clean images to real degraded images, and interpolate between the obtained degraded LR image and its clean LR counterpart. This interpolated LR image is then used along with it’s corresponding HR counterpart to train the super-resolution network from end作者: 違抗 時間: 2025-3-22 22:19 作者: 向外才掩飾 時間: 2025-3-23 03:54 作者: landfill 時間: 2025-3-23 09:16
The Economics of Counterfeit Tradenetwork architectures. We show through extensive experimentation that several networks, while trained on the same dataset and enjoying comparable accuracy, do not necessarily perform similarly in semantic robustness. For example, InceptionV3 is more accurate despite being less semantically robust th作者: Alveolar-Bone 時間: 2025-3-23 10:39 作者: boisterous 時間: 2025-3-23 17:55
The Supply and Control of Offences,, even the best models exhibit a reduction in AUC scores in detecting OoD data. We hypothesise that the sensitivity of neural networks to unseen inputs could be a multi-factor phenomenon arising from the different architectural design choices often amplified by the curse of dimensionality. Prelimina作者: 凝結(jié)劑 時間: 2025-3-23 21:28 作者: 假裝是我 時間: 2025-3-24 02:04
The Regulation of Crowdfunding in Europealuated through its transferability and resiliency against a recent adversarial defense algorithm. Experiments show that the proposed attack is effective against the defense algorithm and is also transferable across CNNs.作者: 乞丐 時間: 2025-3-24 03:50
The Regulation of Crowdfunding in Europedation GAN to convert bicubically downsampled clean images to real degraded images, and interpolate between the obtained degraded LR image and its clean LR counterpart. This interpolated LR image is then used along with it’s corresponding HR counterpart to train the super-resolution network from end作者: 愛哭 時間: 2025-3-24 09:53 作者: NAVEN 時間: 2025-3-24 13:02
Defenses Against Multi-sticker Physical Domain Attacks on Classifiersotect against multi-sticker attacks. We present defensive strategies capable of operating when the defender has full, partial, and no prior information about the attack. By conducting extensive experiments, we show that our proposed defenses can outperform existing defenses against physical attacks when presented with a multi-sticker attack.作者: Ordeal 時間: 2025-3-24 18:22 作者: chlorosis 時間: 2025-3-24 20:59 作者: Cosmopolitan 時間: 2025-3-25 03:08
Ga?l Leboeuf,Armin Schwienbacherures that improves open set robustness without a background dataset. Our method achieves state-of-the-art results on open set classification baselines and easily scales to large-scale open set classification problems.作者: FAR 時間: 2025-3-25 03:22
https://doi.org/10.1007/978-3-642-70252-5ement algorithms is also introduced. The model is trained and evaluated on three mainstream public benchmark datasets, and detailed analysis and comparison of the results are provided which shows that the model achieves state-of-the-art results with less complexity. The model can make inference on . pixel full image in 0.5?s.作者: 不近人情 時間: 2025-3-25 09:57 作者: mighty 時間: 2025-3-25 15:24 作者: 空氣傳播 時間: 2025-3-25 16:17
A Subpixel Residual U-Net and Feature Fusion Preprocessing for Retinal Vessel Segmentationement algorithms is also introduced. The model is trained and evaluated on three mainstream public benchmark datasets, and detailed analysis and comparison of the results are provided which shows that the model achieves state-of-the-art results with less complexity. The model can make inference on . pixel full image in 0.5?s.作者: 樹膠 時間: 2025-3-25 21:50 作者: Buttress 時間: 2025-3-26 01:28
https://doi.org/10.1007/978-3-540-77835-6d landscape. We observe that a subset of adversarial defense techniques results in a similar effect of flattening the likelihood landscape. We further explore directly regularizing towards a flat landscape for adversarial robustness.作者: 沒花的是打擾 時間: 2025-3-26 04:46 作者: Paradox 時間: 2025-3-26 08:37
Crowdfunding as a New Financing Toolrameters lead to the divergence of saliency maps generated by input perturbations. We experimentally reveal inconsistencies among a selection of input perturbation methods and find that they lack robustness for generating saliency maps and for evaluating saliency maps as saliency metrics.作者: 閑蕩 時間: 2025-3-26 15:14
Ga?l Leboeuf,Armin Schwienbacherosed-set attacks and several direct random-search based attacks proposed here. Extensive experiments demonstrate that ReID and FR models are also vulnerable to adversarial attack, and highlight a potential AI trustworthiness problem for these socially important applications.作者: 手工藝品 時間: 2025-3-26 18:32 作者: 機警 時間: 2025-3-26 23:53 作者: 卷發(fā) 時間: 2025-3-27 02:37
Adversarial Shape Perturbations on 3D Point Clouds shape represented by a point cloud. We explore three possible shape attacks for attacking 3D point cloud classification and show that some of them are able to be effective even against preprocessing steps, like the previously proposed point-removal defenses. (Source code available at .).作者: 條約 時間: 2025-3-27 05:31 作者: amphibian 時間: 2025-3-27 11:59 作者: LEVER 時間: 2025-3-27 14:53 作者: 一個姐姐 時間: 2025-3-27 19:18
0302-9743 he 16th European Conference on Computer Vision, ECCV 2020. The conference was planned to take place in Glasgow, UK, during August 23-28, 2020, but changed to a virtual format due to the COVID-19 pandemic...The 249 full papers, 18 short papers, and 21 further contributions included in the workshop pr作者: Banquet 時間: 2025-3-28 01:29
Crowdfunding as a New Financing Toolotect against multi-sticker attacks. We present defensive strategies capable of operating when the defender has full, partial, and no prior information about the attack. By conducting extensive experiments, we show that our proposed defenses can outperform existing defenses against physical attacks when presented with a multi-sticker attack.作者: Lignans 時間: 2025-3-28 05:05 作者: HARP 時間: 2025-3-28 09:57
A Deep Dive into Adversarial Robustness in Zero-Shot Learningsuccess, it has been shown multiple times that machine learning models are prone to imperceptible perturbations that can severely degrade their accuracy. So far, existing studies have primarily focused on models where supervision across all classes were available. In contrast, Zero-shot Learning (ZS作者: duplicate 時間: 2025-3-28 13:56 作者: 變異 時間: 2025-3-28 15:16 作者: Introduction 時間: 2025-3-28 21:07
Deep ,-NN Defense Against Clean-Label Data Poisoning Attacks minimally-perturbed samples into the training data, causing a model to misclassify a particular test sample during inference. Although defenses have been proposed for general poisoning attacks, no reliable defense for clean-label attacks has been demonstrated, despite the attacks’ effectiveness and作者: arcane 時間: 2025-3-29 02:06 作者: 重疊 時間: 2025-3-29 03:51 作者: ONYM 時間: 2025-3-29 09:17
Jacks of All Trades, Masters of None: Addressing Distributional Shift and Obtrusiveness via Transparccess and obtrusiveness via the design of novel semi-transparent patches. This work is motivated by our pursuit of a systematic performance analysis of patch attack robustness with regard to geometric transformations. Specifically, we first elucidate a) key factors underpinning patch attack success 作者: Needlework 時間: 2025-3-29 15:20 作者: scrape 時間: 2025-3-29 18:31 作者: 卡死偷電 時間: 2025-3-29 20:25
WaveTransform: Crafting Adversarial Examples via Input Decompositionformation present in images have been extracted and learnt by a host of representation learning techniques, including deep learning. Inspired by this observation, we introduce a novel class of adversarial attacks, namely ‘WaveTransform’, that creates adversarial noise corresponding to low-frequency 作者: spondylosis 時間: 2025-3-30 02:32
Robust Super-Resolution of Real Faces Using Smooth Featuresependent noises. So, in order to successfully super-resolve real faces, a method needs to be robust to a wide range of noise, blur, compression artifacts etc. Some of the recent works attempt to model these degradations from a dataset of real images using a Generative Adversarial Network (GAN). They作者: 種類 時間: 2025-3-30 06:43
Improved Robustness to Open Set Inputs via Tempered Mixupent for training. However, real-world classifiers must handle inputs that are far from the training distribution including samples from unknown classes. Open set robustness refers to the ability to properly label samples from previously unseen categories as novel and avoid high-confidence, incorrect作者: Ointment 時間: 2025-3-30 10:16
Defenses Against Multi-sticker Physical Domain Attacks on Classifiersykholt et al. can fool a classifier by placing black and white stickers on an object such as a road sign. While this attack may pose a significant threat to visual classifiers, there are currently no defenses designed to protect against this attack. In this paper, we propose new defenses that can pr作者: 確定方向 時間: 2025-3-30 14:32
Adversarial Attack on Deepfake Detection Using RL Based Texture Patchespolitical opinion, recent research has focused on ways to better detect deepfake videos. Even though researchers have had some success in detecting deepfake videos, it has been found that these detection systems can be attacked..The key contributions of this paper are (a) a deepfake dataset created 作者: 外來 時間: 2025-3-30 20:37
A Subpixel Residual U-Net and Feature Fusion Preprocessing for Retinal Vessel Segmentation extraction of the vessels is vital for computer-aided diagnostic systems to provide a speedy and precise diagnosis. This paper introduces SpruNet, a Subpixel Convolution based Residual U-Net architecture which re-purposes subpixel convolutions as down-sampling and up-sampling method. The proposed s作者: JOT 時間: 2025-3-30 23:55
Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentationeworks have shown high performance in image segmentation. In this paper, we propose Attention Deeplabv3+, an extended version of Deeplabv3+ for skin lesion segmentation by employing the idea of attention mechanism in two stages. We first capture the relationship between the channels of a set of feat作者: grandiose 時間: 2025-3-31 01:40 作者: BACLE 時間: 2025-3-31 08:09
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234239.jpg作者: squander 時間: 2025-3-31 09:41 作者: ascetic 時間: 2025-3-31 15:59
