Optical transport networks (OTNs) are widely used in backbone- and metro-area transmission networks to increase network transmission capacity. In the OTN, it is particularly crucial to rationally allocate routes and maximize network capacities. By employing deep reinforcement learning (DRL)- and software-defined networking (SDN)-based solutions, the capacity of optical networks can be effectively increased. However, because most DRL-based routing optimization methods have low sample usage and difficulty in coping with sudden network connectivity changes, converging in software-defined OTN scenarios is challenging. Additionally, the generalization ability of these methods is weak. This paper proposes an ensembles- and message-passing neural-network-based Deep Q-Network (EMDQN) method for optical network routing optimization to address this problem. To effectively explore the environment and improve agent performance, the multiple EMDQN agents select actions based on the highest upper-confidence bounds. Furthermore, the EMDQN agent captures the network’s spatial feature information using a message passing neural network (MPNN)-based DRL policy network, which enables the DRL agent to have generalization capability. The experimental results show that the EMDQN algorithm proposed in this paper performs better in terms of convergence. EMDQN effectively improves the throughput rate and link utilization of optical networks and has better generalization capabilities.

Video watermarking plays a vital role in protecting the video copyright. The quantization-based methods are widely used in the existing watermarking algorithms, owing to their low computational complexity and completely blind extraction. However, most of them work poorly in resisting scaling attacks, by which the quantization value may fall outside the original quantization interval. For addressing this issue, an adaptive quantization index modulation method is proposed. The property that is associated with the ratio of the DC coefficient before and after scaling the video resolution motivates us to select the DC coefficient as the quantization value and set the size of the quantization interval by the video resolution to maintain the synchronization between them before and after scaling. Moreover, a strategy taking advantage of the high decoding reliability of the QRCode is proposed to terminate the extraction in advance, and both the embedding and the extracting process are performed in the spatial domain, which all contribute to further enhance the execution efficiency. The experimental results show that our algorithm outperforms the state-of-the-art method in terms of imperceptibility, robustness, and computational cost.

Traffic signs recognition (TSR) plays an essential role in assistant driving and intelligent transportation system. However, the noise of complex environment may lead to motion-blur or occlusion problems, which raise the tough challenge to real-time recognition with high accuracy and robust. In this article, we propose IECES-network which with improved encoders and Siamese net. The three-stage approach of our method includes Efficient-CNN based encoders, Siamese backbone and the fully-connected layers. We firstly use convolutional encoders to extract and encode the traffic sign features of augmented training samples and standard images. Then, we design the Siamese neural network with Efficient-CNN based encoder and contrastive loss function, which can be trained to improve the robustness of TSR problem when facing the samples of motion-blur and occlusion by computing the distance between inputs and templates. Additionally, the template branch of the proposed network can be stopped when executing the recognition tasks after training to raise the process speed of our real-time model, and alleviate the computational resource and parameter scale. Finally, we recombined the feature code and a fully-connected layer with SoftMax function to classify the codes of samples and recognize the category of traffic signs. The results of experiments on the Tsinghua-Tencent 100K dataset and the German Traffic Sign Recognition Benchmark dataset demonstrate the performance of the proposed IECES-network. Compared with other state-of-the-art methods, in the case of motion-blur and occluded environment, the proposed method achieves competitive performance precision-recall and accuracy metric average is 88.1%, 86.43% and 86.1% with a 2.9M lightweight scale, respectively. Moreover, processing time of our model is 0.1s per frame, of which the speed is increased by 1.5 times compared with existing methods.

As the Internet and communication technologies have developed quickly, the spread and usage of online video content have become easier, which results in major infringement problems. While video watermarking may be a viable solution for digital video content copyright protection, overcoming geometric attacks is a significant challenge. Although feature point‐based watermarking algorithms are expected to be very resistant to these attacks, they are sensitive to feature region localization errors, resulting in poor watermark extraction accuracy. To solve this issue, we introduce the template to enhance the location accuracy of feature point‐based watermarking. Furthermore, a scene change‐based frame allocation method is presented, which arranges the template and the watermark to be embedded into different frames and eliminates their mutual interference, enhancing the performance of the proposed algorithm. According to the experimental results, our algorithm outperforms state‐of‐the‐art methods in terms of robustness against geometric attacks under close imperceptibility.