FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313389185> ?p ?o ?g. }

• W4313389185 endingPage "209" @default.
• W4313389185 startingPage "209" @default.
• W4313389185 abstract "At present, deep learning has achieved excellent achievements in image processing and computer vision and is widely used in the field of watermarking. Attention mechanism, as the research hot spot of deep learning, has not yet been applied in the field of watermarking. In this paper, we propose a deep learning and attention network for robust image watermarking (DARI-Mark). The framework includes four parts: an attention network, a watermark embedding network, a watermark extraction network, and an attack layer. The attention network used in this paper is the channel and spatial attention network, which calculates attention weights along two dimensions, channel and spatial, respectively, assigns different weights to pixels in different channels at different positions and is applied in the watermark embedding and watermark extraction stages. Through end-to-end training, the attention network can locate nonsignificant areas that are insensitive to the human eye and assign greater weights during watermark embedding, and the watermark embedding network selects this region to embed the watermark and improve the imperceptibility. In watermark extraction, by setting the loss function, larger weights can be assigned to watermark-containing features and small weights to noisy signals, so that the watermark extraction network focuses on features about the watermark and suppresses noisy signals in the attacked image to improve robustness. To avoid the phenomenon of gradient disappearance or explosion when the network is deep, both the embedding network and the extraction network have added residual modules. Experiments show that DARI-Mark can embed the watermark without affecting human subjective perception and that it has good robustness. Compared with other state-of-the-art watermarking methods, the proposed framework is more robust to JPEG compression, sharpening, cropping, and noise attacks." @default.
• W4313389185 created "2023-01-06" @default.
• W4313389185 creator A5015144801 @default.
• W4313389185 creator A5036693904 @default.
• W4313389185 creator A5066934378 @default.
• W4313389185 creator A5067676505 @default.
• W4313389185 date "2022-12-31" @default.
• W4313389185 modified "2023-10-01" @default.
• W4313389185 title "DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking" @default.
• W4313389185 cites W1649208874 @default.
• W4313389185 cites W1840447660 @default.
• W4313389185 cites W2070251342 @default.
• W4313389185 cites W2143037525 @default.
• W4313389185 cites W22271197 @default.
• W4313389185 cites W2337678523 @default.
• W4313389185 cites W2555753423 @default.
• W4313389185 cites W2560266880 @default.
• W4313389185 cites W2566532372 @default.
• W4313389185 cites W2607126301 @default.
• W4313389185 cites W2752782242 @default.
• W4313389185 cites W2789267863 @default.
• W4313389185 cites W2982163850 @default.
• W4313389185 cites W2997526307 @default.
• W4313389185 cites W2997594966 @default.
• W4313389185 cites W3021659241 @default.
• W4313389185 cites W3028710606 @default.
• W4313389185 cites W3035671550 @default.
• W4313389185 cites W3049492707 @default.
• W4313389185 cites W3089622076 @default.
• W4313389185 cites W3106527215 @default.
• W4313389185 cites W3138618072 @default.
• W4313389185 cites W3169979745 @default.
• W4313389185 cites W3177191614 @default.
• W4313389185 cites W3184769389 @default.
• W4313389185 cites W3206855633 @default.
• W4313389185 cites W4224218785 @default.
• W4313389185 doi "https://doi.org/10.3390/math11010209" @default.
• W4313389185 hasPublicationYear "2022" @default.
• W4313389185 type Work @default.
• W4313389185 citedByCount "2" @default.
• W4313389185 countsByYear W43133891852023 @default.
• W4313389185 crossrefType "journal-article" @default.
• W4313389185 hasAuthorship W4313389185A5015144801 @default.
• W4313389185 hasAuthorship W4313389185A5036693904 @default.
• W4313389185 hasAuthorship W4313389185A5066934378 @default.
• W4313389185 hasAuthorship W4313389185A5067676505 @default.
• W4313389185 hasBestOaLocation W43133891851 @default.
• W4313389185 hasConcept C104317684 @default.
• W4313389185 hasConcept C108583219 @default.
• W4313389185 hasConcept C115961682 @default.
• W4313389185 hasConcept C150817343 @default.
• W4313389185 hasConcept C153180895 @default.
• W4313389185 hasConcept C154945302 @default.
• W4313389185 hasConcept C160633673 @default.
• W4313389185 hasConcept C164112704 @default.
• W4313389185 hasConcept C185592680 @default.
• W4313389185 hasConcept C31972630 @default.
• W4313389185 hasConcept C41008148 @default.
• W4313389185 hasConcept C41608201 @default.
• W4313389185 hasConcept C55493867 @default.
• W4313389185 hasConcept C63479239 @default.
• W4313389185 hasConceptScore W4313389185C104317684 @default.
• W4313389185 hasConceptScore W4313389185C108583219 @default.
• W4313389185 hasConceptScore W4313389185C115961682 @default.
• W4313389185 hasConceptScore W4313389185C150817343 @default.
• W4313389185 hasConceptScore W4313389185C153180895 @default.
• W4313389185 hasConceptScore W4313389185C154945302 @default.
• W4313389185 hasConceptScore W4313389185C160633673 @default.
• W4313389185 hasConceptScore W4313389185C164112704 @default.
• W4313389185 hasConceptScore W4313389185C185592680 @default.
• W4313389185 hasConceptScore W4313389185C31972630 @default.
• W4313389185 hasConceptScore W4313389185C41008148 @default.
• W4313389185 hasConceptScore W4313389185C41608201 @default.
• W4313389185 hasConceptScore W4313389185C55493867 @default.
• W4313389185 hasConceptScore W4313389185C63479239 @default.
• W4313389185 hasIssue "1" @default.
• W4313389185 hasLocation W43133891851 @default.
• W4313389185 hasLocation W43133891852 @default.
• W4313389185 hasOpenAccess W4313389185 @default.
• W4313389185 hasPrimaryLocation W43133891851 @default.
• W4313389185 hasRelatedWork W1539296833 @default.
• W4313389185 hasRelatedWork W1917245318 @default.
• W4313389185 hasRelatedWork W2028320499 @default.
• W4313389185 hasRelatedWork W2068060394 @default.
• W4313389185 hasRelatedWork W2148446898 @default.
• W4313389185 hasRelatedWork W2367449261 @default.
• W4313389185 hasRelatedWork W2382181716 @default.
• W4313389185 hasRelatedWork W2598996165 @default.
• W4313389185 hasRelatedWork W4205836422 @default.
• W4313389185 hasRelatedWork W4293251151 @default.
• W4313389185 hasVolume "11" @default.
• W4313389185 isParatext "false" @default.
• W4313389185 isRetracted "false" @default.
• W4313389185 workType "article" @default.