The CLP Publishing collects CLP journals and partnered optics and photonics journals, and provides readers an optical publishing platform with global influence. SPIE, OSA, CUP. Chinese Laser Press (CLP), established by Shanghai Institute of Optics and Fine Mechanics (SIOM), Chinese Academy of Sciences (CAS) and Chinese Optical Society (COS) in 2009, nowadays publishes eight journals and manages three online platforms.

The serious concerns over the negative impacts of Deepfakes have attracted wide attentions in the community of multimedia forensics. The existing detection works achieve deepfake detection by improving the traditional backbone networks to capture subtle manipulation traces. However, there is no attempt to construct new backbone networks with different structures for Deepfake detection by improving the internal feature representation of convolution. In this work, we propose a novel Space-Frequency Interactive Convolution (SFIConv) to efficiently model the manipulation clues left by Deepfake. To obtain high-frequency features from tampering traces, a Multichannel Constrained Separable Convolution (MCSConv) is designed as the component of the proposed SFIConv, which learns space-frequency features via three stages, namely generation, interaction and fusion. In addition, SFIConv can replace the vanilla convolution in any backbone networks without changing the network structure. Extensive experimental results show that seamlessly equipping SFIConv into the backbone network greatly improves the accuracy for Deepfake detection. In addition, the space-frequency interaction mechanism does benefit to capturing common artifact features, thus achieving better results in cross-dataset evaluation. Our code will be available at https://github.com/EricGzq/SFIConv .

Nowadays Practical Byzantine Fault Tolerance (PBFT) algorithm has become the most extensive consensus algorithm in the alliance chain. However, the PBFT algorithm is usually only applicable to small networks due to high communication complexity and poor scalability. Although there have been many improved algorithms for PBFT in recent years, they ignore fault tolerance and democracy. Therefore, to meet the requirements of a high degree of decentralization and fault tolerance of blockchain-based scenarios. This paper proposes a high fault tolerance consensus algorithm NBFT, which follows the principle of decentralization and democratization of blockchain and ensures the improvement of performance in fault tolerance upper limit and scalability. First, we use the consistent hash algorithm to group the consensus nodes to avoid much communication between nodes, reduce the communication complexity of the network, and improve the scalability of the network. Second, to ensure the fault-tolerant ability of the grouping consensus, the nodal decision broadcast model and threshold vote-counting model are proposed first. Combined with the proposed two models, the joint fault analysis of nodes is carried out, and the fault tolerance upper limit is more than 1/3. Then, the Faulty Number Determined (FND) model is introduced to simulate the experiment, and the results are verified.

Synthetic aperture radar (SAR) change detection provides a powerful tool for continuous, reliable, and objective observation of the Earth, supporting a wide range of applications that require regular monitoring and assessment of changes in the natural and built environment. In this paper, we introduce a novel SAR image change detection method based on principal component analysis and two-level clustering. First, two difference images of the log-ratio and mean-ratio operators are computed, then the principal component analysis fusion model is used to fuse the two difference images, and a new difference image is generated. To incorporate contextual information during the feature extraction phase, Gabor wavelets are used to obtain the representation of the difference image across multiple scales and orientations. The maximum magnitude across all orientations at each scale is then concatenated to form the Gabor feature vector. Following this, a cascading clustering algorithm is developed within this discriminative feature space by merging the first-level fuzzy c-means clustering with the second-level neighbor rule. Ultimately, the two-level combination of the changed and unchanged results produces the final change map. Five SAR datasets are used for the experiment, and the results show that our algorithm has significant advantages in SAR change detection.