標(biāo)題: Titlebook: Digital Watermarking for Machine Learning Model; Techniques, Protocol Lixin Fan,Chee Seng Chan,Qiang Yang Book 2023 The Editor(s) (if appli [打印本頁] 作者: 大腦 時間: 2025-3-21 17:41
書目名稱Digital Watermarking for Machine Learning Model影響因子(影響力)
書目名稱Digital Watermarking for Machine Learning Model影響因子(影響力)學(xué)科排名
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書目名稱Digital Watermarking for Machine Learning Model網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Digital Watermarking for Machine Learning Model被引頻次學(xué)科排名
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書目名稱Digital Watermarking for Machine Learning Model年度引用學(xué)科排名
書目名稱Digital Watermarking for Machine Learning Model讀者反饋
書目名稱Digital Watermarking for Machine Learning Model讀者反饋學(xué)科排名
作者: sultry 時間: 2025-3-21 23:31
Ownership Verification Protocols for Deep Neural Network Watermarkschemes, formulates several additional requirements regarding ownership proof under elementary protocols, and puts forward the necessity of analyzing and regulating the ownership verification procedure.作者: inundate 時間: 2025-3-22 03:55 作者: 拘留 時間: 2025-3-22 07:47
Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Bo which are used to fingerprint the model. Another model is likely to be a pirated version of the owner’s model if they have the same predictions for most fingerprinting data points. The key difference between fingerprinting and watermarking is that fingerprinting . fingerprint that characterizes the作者: 侵蝕 時間: 2025-3-22 11:31 作者: Migratory 時間: 2025-3-22 16:41
Protecting Recurrent Neural Network by Embedding Keyss to train RNN models in a specific way such that when an invalid or forged key is presented, the performance of the embedded RNN models will be .. Having said that, the key gate was inspired by the nature of RNN model, to govern the flow of hidden state and designed in such a way that no additional作者: Migratory 時間: 2025-3-22 19:02
Model Auditing for Data Intellectual Propertyata owner cannot manage and thus cannot provide meaningful data ownership resolution. In this chapter, we rigorously present the model auditing problem for data ownership and open a new revenue in this area of research.作者: 頑固 時間: 2025-3-22 22:22 作者: 1分開 時間: 2025-3-23 03:52
https://doi.org/10.1057/9781137006509ermine whether a suspicious model is stolen from the victim, based on model gradients. The final ownership verification is judged by hypothesis test. Extensive experiments on CIFAR-10 and ImageNet datasets verify the effectiveness of our defense under both centralized training and federated learning作者: TRACE 時間: 2025-3-23 06:47 作者: 材料等 時間: 2025-3-23 12:05 作者: 滋養(yǎng) 時間: 2025-3-23 15:09 作者: 認識 時間: 2025-3-23 19:45
Early Modern Cultural Studies 1500–1700ata owner cannot manage and thus cannot provide meaningful data ownership resolution. In this chapter, we rigorously present the model auditing problem for data ownership and open a new revenue in this area of research.作者: Insubordinate 時間: 2025-3-23 23:11
ding image classification, image generation, image captions, natural language processing and reinforcement learning.?? ..This book covers the motivations, fundamentals, techniques and protocols for protecting M978-981-19-7556-1978-981-19-7554-7作者: Anticoagulants 時間: 2025-3-24 02:40 作者: Abnormal 時間: 2025-3-24 09:31 作者: 游行 時間: 2025-3-24 13:54
Protecting Image Processing Networks via Model Watermarking networks. In this chapter, we propose a new model watermarking framework for image processing networks. Under the framework, two strategies are further developed, namely, the model-agnostic strategy and the model-specific strategy. The proposed watermarking method performs well in terms of fidelity, capacity, and robustness.作者: 送秋波 時間: 2025-3-24 15:14 作者: 解決 時間: 2025-3-24 20:00
Book 2023egard to intellectual property rights (IPR).? Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model’s owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniqu作者: 土坯 時間: 2025-3-24 23:18
Anterior Design: Presenting the Past in ,esent a black-box watermarking approach for pre-trained models, which exploits the over-parameterization property of an image recovery DNN. Moreover, a watermark visualization method is introduced for additional subjective verification.作者: 共同確定為確 時間: 2025-3-25 05:43
Isabelle de France, Child Bride model. The effectiveness of our proposed approach to withstand different attacks without compromising the original image captioning performance has been demonstrated by the experiments on the MS-COCO and Flickr30k datasets.作者: 鈍劍 時間: 2025-3-25 08:53 作者: 爭議的蘋果 時間: 2025-3-25 12:44 作者: Mendacious 時間: 2025-3-25 19:05 作者: 是比賽 時間: 2025-3-25 21:04 作者: adduction 時間: 2025-3-26 02:20
Shakespeare and the Question of Culturermarks in federated learning models for legitimate IPR. In the proposed FedIPR scheme, each client independently extracts the watermarks and claims ownership on the federated learning model while keep training data and watermark private.作者: 淡紫色花 時間: 2025-3-26 04:24
Introductionin all kinds of applications including finance, healthcare, public transportation, etc., reforming our lives in a unprecedentedly profound manner. On the other hand, the wide applicability of these machine learning models calls for appropriate managements of these models to ensure their use comply w作者: 尊敬 時間: 2025-3-26 11:25
Ownership Verification Protocols for Deep Neural Network Watermarkses, spearheaded by the watermark, have been proposed to establish the connection between a deep neural network and its owner; however, it is until that such connection is provably unambiguous and unforgeable that it can be leveraged for copyright protection. The ownership proof is feasible only afte作者: 摻和 時間: 2025-3-26 15:44
Model Watermarking for Deep Neural Networks of Image Recoveryd DNN models of image recovery has become popular in the society. As a result, how to protect the intellectual property of the owners of those models has been a serious concern. To address it, this chapter introduces a framework developed in our recent work for watermarking the DNN models of image r作者: 老巫婆 時間: 2025-3-26 18:52 作者: FLASK 時間: 2025-3-26 21:27
Protecting Intellectual Property of Machine Learning Models via Fingerprinting the Classification Bos such that it does not need to train its own model, which requires a large amount of resources. Therefore, it becomes an urgent problem how to distinguish such compromise of IP. Watermarking has been widely adopted as a solution in the literature. However, watermarking requires modification of the 作者: 消毒 時間: 2025-3-27 03:49 作者: 水槽 時間: 2025-3-27 06:38
Watermarks for Deep Reinforcement Learninging models, various watermarking approaches have been proposed. However, considering the complexity and stochasticity of reinforcement learning tasks, we cannot apply existing watermarking techniques for deep learning models to the deep reinforcement learning scenario directly. Existing watermarking作者: Mawkish 時間: 2025-3-27 09:57
Ownership Protection for Image Captioning Modelster, we demonstrate that image captioning tasks cannot be adequately protected by the present digital watermarking architecture, which are generally considered as one of the most difficult AI challenges. To safeguard the image captioning model, we propose two distinct embedding strategies in the rec作者: remission 時間: 2025-3-27 17:12 作者: 法律的瑕疵 時間: 2025-3-27 20:40
FedIPR: Ownership Verification for Federated Deep Neural Network Modelsbution, and free-riding threat the collaboratively built models in federated learning. To address IP infringement issues, in this chapter, we introduce a novel deep neural network ownership verification framework for secure federated learning that allows each client to embed and extract private wate作者: 幸福愉悅感 時間: 2025-3-28 00:30
Model Auditing for Data Intellectual Propertye the model developer may illegally misuse or steal other party’s private data for training. To determine the data ownership from a trained deep neural network model, in this chapter, we propose a deep neural network auditing scheme that allows the auditor to trace illegal data usage from a trained 作者: 你不公正 時間: 2025-3-28 04:06
Lixin Fan,Chee Seng Chan,Qiang YangThe first book to address the use of digital watermarking for verifying machine learning model ownerships.Presents essential protocols, methodologies and techniques for protecting machine learning mod作者: 惡名聲 時間: 2025-3-28 08:29 作者: Alcove 時間: 2025-3-28 11:27
https://doi.org/10.1007/978-981-19-7554-7Machine learning model protection; deep learning model protection; model ownerhsip verification; model 作者: 為現(xiàn)場 時間: 2025-3-28 17:16 作者: Lucubrate 時間: 2025-3-28 19:54 作者: crease 時間: 2025-3-29 00:55
https://doi.org/10.1057/9780230372917es, spearheaded by the watermark, have been proposed to establish the connection between a deep neural network and its owner; however, it is until that such connection is provably unambiguous and unforgeable that it can be leveraged for copyright protection. The ownership proof is feasible only afte作者: B-cell 時間: 2025-3-29 06:07 作者: analogous 時間: 2025-3-29 08:20
https://doi.org/10.1057/9781137006509lectual properties of their owners. However, recent literature revealed that the adversaries can easily “steal” models by acquiring their function-similar copy, even when they have no training samples and information about the victim models. In this chapter, we introduce a robust and harmless model 作者: tinnitus 時間: 2025-3-29 13:59
https://doi.org/10.1057/9781137006509s such that it does not need to train its own model, which requires a large amount of resources. Therefore, it becomes an urgent problem how to distinguish such compromise of IP. Watermarking has been widely adopted as a solution in the literature. However, watermarking requires modification of the 作者: photopsia 時間: 2025-3-29 19:37
https://doi.org/10.1057/9781137006509(IP) of such valuable image processing networks, the model vendor can sell the service in the manner of the application program interface (API). However, even if the attacker can only query the API, he is still able to conduct model extraction attacks, which can steal the functionality of the target
