Training Convolutional Neural Networks withMulti-Size Images and Triplet Loss for RemoteSensing Scene Classification.

Many remote sensing scene classification algorithms improve their classification accuracyby additional modules, which increases the parameters and computing overhead of the model atthe inference stage. In this paper, we explore how to improve the classification accuracy of themodel without adding modules at the inference stage. First, we propose a network trainingstrategy of training with multi-size images. Then, we introduce more supervision information bytriplet loss and design a branch for the triplet loss. In addition, dropout is introduced between thefeature extractor and the classifier to avoid over-fitting. These modules only work at the trainingstage and will not bring about the increase in model parameters at the inference stage. We useResnet18 as the baseline and add the three modules to the baseline. We perform experiments onthree datasets: AID, NWPU-RESISC45, and OPTIMAL. Experimental results show that our modelcombined with the three modules is more competitive than many existing classification algorithms.In addition, ablation experiments on OPTIMAL show that dropout, triplet loss, and training withmulti-size images improve the overall accuracy of the model on the test set by 0.53%, 0.38%, and0.7%, respectively. The combination of the three modules improves the overall accuracy of themodel by 1.61%. It can be seen that the three modules can improve the classification accuracy of themodel without increasing model parameters at the inference stage, and training with multi-sizeimages brings a greater gain in accuracy than the other two modules, but the combination of thethree modules will be better.

Reusable Mesh Signature Scheme for Protecting Identity Privacy of IoT Devices.

The development of the Internet of Things (IoT) plays a very important role for processing data at the edge of a network. Therefore, it is very important to protect the privacy of IoT devices when these devices process and transfer data. A mesh signature (MS) is a useful cryptographic tool, which makes a signer sign any message anonymously. As a result, the signer can hide his specific identity information to the mesh signature, namely his identifying information (such as personal public key) may be hidden to a list of tuples that consist of public key and message. Therefore, we propose an improved mesh signature scheme for IoT devices in this paper. The IoT devices seen as the signers may sign their publishing data through our proposed mesh signature scheme, and their specific identities can be hidden to a list of possible signers. Additionally, mesh signature consists of some atomic signatures, where the atomic signatures can be reusable. Therefore, for a large amount of data published by the IoT devices, the atomic signatures on the same data can be reusable so as to decrease the number of signatures generated by the IoT devices in our proposed scheme. Compared with the original mesh signature scheme, the proposed scheme has less computational costs on generating final mesh signature and signature verification. Since atomic signatures are reusable, the proposed scheme has more advantages on generating final mesh signature by reconstructing atomic signatures. Furthermore, according to our experiment, when the proposed scheme generates a mesh signature on 10 MB message, the memory consumption is only about 200 KB. Therefore, it is feasible that the proposed scheme is used to protect the identity privacy of IoT devices.