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Robust watermarking tries to conceal information within a cover image/video imperceptibly that is resistant to various distortions. Recently, deep learning-based approaches for image watermarking have made significant advancements in robustness and invisibility. However, few studies focused on video watermarking using deep neural networks due to the high complexity and computational costs. Our paper aims to answer this research question: Can well-designed deep learning-based image watermarking be efficiently adapted to video watermarking? Our answer is positive. First, we revisit the workflow of deep learning-based watermarking methods that leads to a critical insight: temporal information in the video may be essential for general computer vision tasks but not for specific video watermarking. Inspired by this insight, we propose a method named ItoV for efficiently adapting deep learning-based Image watermarking to Video watermarking. Specifically, ItoV merges the temporal dimension of the video with the channel dimension to enable deep neural networks to treat videos as images. We further explore the effects of different convolutional blocks in video watermarking. We find that spatial convolution is the primary influential component in video watermarking and depthwise convolutions significantly reduce computational cost with negligible impact on performance. In addition, we propose a new frame loss to constrain that the watermark intensity in each video clip frame is consistent, significantly improving the invisibility. Extensive experiments show the superior performance of the adapted video watermarking method compared with the state-of-the-art methods on Kinetics-600 and Inter4K datasets, which demonstrate the efficacy of our method ItoV.

Image watermarking is a technique for hiding information into images that can withstand distortions while requiring the encoded image to be perceptually identical to the original image. Recent work based on deep neural networks (DNN) has achieved impressive progression in digital watermarking. Higher robustness under various distortions is the eternal pursuit of digital image watermarking approaches. In this paper, we propose DBMARK, a novel end-to-end digital image watermarking framework to deep boost the robustness of DNN-based image watermarking. The key novelty is the synergy of invertible neural networks (INN) and effective watermark features generation. The framework generates watermark features with redundancy and error correction ability through the effective neural network based message processor, synergized with the powerful information embedding and extraction abilities of INN to achieve higher robustness and invisibility. The powerful learning ability of neural networks enables the message processor to adapt to various distortions. In addition, we propose to embed the watermark information in the discrete wavelet transform (DWT) domain and design low-low (LL) sub-band loss to enhance invisibility. Extensive experiment results demonstrate the superiority of the proposed framework compared with the state-of-the-art ones under various distortions such as dropout, cropout, crop, Gaussian filter, and JPEG compression.

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Payment channel is a class of techniques designed to solve the scalability problem of blockchain. By establishing channels off the blockchain to form payment channel networks (PCNs), users can make instant payments without interacting with the blockchain, avoiding the problems of long transaction consensus delays and high transaction fees. Recently, the optimization of PCNs has mainly focused on improving the network throughput via multi-path routing. However, the transaction's atomicity comes at a non-trivial cost for transaction completion latency that affects user experience in deadline-sensitive applications of PCNs. In this paper, we propose a new and systematic framework DPCN to consider the deadlines of transactions for payment channel networks while improving the success ratio of transactions. DPCN is enabled via a synergy of three components: (1) deadline-based dynamic transaction split mechanism that splits the transaction according to current network status and the transaction's deadline; (2) deadline-aware transaction scheduling that prioritizes near-deadline transactions; (3) deadline-aware transaction congestion avoidance algorithm, which uses a path window to balance transactions with different deadlines. Our extensive experiments show that compared with existing methods, DPCN can well meet the needs of transactions with different deadlines and ensure a higher success ratio for transactions in the payment channel networks.

With the rapid development of smart cities, white light-emitting diodes (LEDs) are widely used in indoor lighting due to their characteristics of energy-saving, long lifetimes, and low cost. At the same time, the high-frequency modulated LEDs allow visible light to be used for indoor communications, positioning, and security. Visible light applications have many advantages, such as avoiding electromagnetic interference, high communication speed, great privacy, and high-precise positioning. In this paper, we will survey the application prospects and research results of visible light from scenarios such as high-speed communication, privacy security, and navigation localization and focus on analyzing and discussing the challenges and development trends encountered in visible light positioning.

Additional file 1. Highlights

UNLABELLED:We evaluated whether dexamethasone augments the osteogenic capability of bone marrow-derived stromal cells (BMSCs) and muscle tissue-derived stromal cells (MuSCs), both of which are thought to contribute to ectopic bone formation induced by bone morphogenetic protein-2 (BMP-2), and determined the underlying mechanisms. Rat BMSCs and MuSCs were cultured in growth media with or without 10-7 M dexamethasone and then differentiated under osteogenic conditions with dexamethasone and BMP-2. The effects of dexamethasone on cell proliferation and osteogenic differentiation, and also on ectopic bone formation induced by BMP-2, were analyzed. Dexamethasone affected not only the proliferation rate but also the subpopulation composition of BMSCs and MuSCs, and subsequently augmented their osteogenic capacity during osteogenic differentiation. During osteogenic induction by BMP-2, dexamethasone also markedly affected cell proliferation in both BMSCs and MuSCs. In an in vivo ectopic bone formation model, bone formation in muscle-implanted scaffolds containing dexamethasone and BMP-2 was more than two fold higher than that in scaffolds containing BMP-2 alone. Our results suggest that dexamethasone potently enhances the osteogenic capability of BMP-2 and may thus decrease the quantity of BMP-2 required for clinical application, thereby reducing the complications caused by excessive doses of BMP-2. HIGHLIGHTS:1. Dexamethasone induced selective proliferation of bone marrow- and muscle-derived cells with higher differentiation potential. 2. Dexamethasone enhanced the osteogenic capability of bone marrow- and muscle-derived cells by altering the subpopulation composition. 3. Dexamethasone augmented ectopic bone formation induced by bone morphogenetic protein-2.

The smartphone ecosystem encompasses smartphones' hardware and software platform, applications (apps) running on top of the platform, as well the infrastructural components. As the smartphone ecosystem is becoming an important part of our daily life, it is essential to profile, understand and, ultimately, secure the devices and the information they collect and manipulate. To this end, we pave the way for understanding and improving the smartphone ecosystem by designing useful tools, performing measurement studies and security analysis.In this dissertation, we describe several key steps that help us understand and improve the Android smartphone ecosystem. First, we present the results of a long-term evolution study on Android permission system, the basic security mechanism in Android OS, is defined and used in practice; our results indicate that the Android permission system is becoming less secure over time. Second, we present a systematic approach and tool, named ProfileDroid, that enables multi-layer profiling of Android apps. ProfileDroid has a myriad of applications including behavioral app fingerprinting, enhancing users' understanding and control of app behavior, improving user experience, assessing performance and security implications. Finally, the Bring Your Own Handheld-device (BYOH) phenomenon presents novel management challenges to network administrators. We propose a systematic approach, Brofiler, for profiling the behavior of BYOHs along four dimensions: (a) protocol and control plane, (b) data plane, (c) temporal behavior, and (d) across dimensions using the H-M-L model by considering the different levels of intensity in each dimension. Using profiles from Brofiler, a network administrator can develop effective policies for managing BYOHs.