TAE-Net: Task-Adaptive Embedding Network for Few-Shot Remote Sensing Scene Classification

Recently, approaches based on deep learning are quite prevalent in the area of remote sensing scene classification. Though significant success has been achieved, these approaches are still subject to an excess of parameters and extremely dependent on a large quantity of labeled data. In this study, few-shot learning is used for remote sensing scene classification tasks. The goal of few-shot learning is to recognize unseen scene categories given extremely limited labeled samples. For this purpose, a novel task-adaptive embedding network is proposed to facilitate few-shot scene classification of remote sensing images, referred to as TAE-Net. A feature encoder is first trained on the base set to learn embedding features of input images in the pre-training phase. Then in the meta-training phase, a new task-adaptive attention module is designed to yield the task-specific attention, which can adaptively select informative embedding features among the whole task. In the end, in the meta-testing phase, the query image derived from the novel set is predicted by the meta-trained model with limited support images. Extensive experiments are carried out on three public remote sensing scene datasets: UC Merced, WHU-RS19, and NWPU-RESISC45. The experimental results illustrate that our proposed TAE-Net achieves new state-of-the-art performance for few-shot remote sensing scene classification.

Task-Adaptive Embedding Learning with Dynamic Kernel Fusion for Few-Shot Remote Sensing Scene Classification

The central goal of few-shot scene classification is to learn a model that can generalize well to a novel scene category (UNSEEN) from only one or a few labeled examples. Recent works in the remote sensing (RS) community tackle this challenge by developing algorithms in a meta-learning manner. However, most prior approaches have either focused on rapidly optimizing a meta-learner or aimed at finding good similarity metrics while overlooking the embedding power. Here we propose a novel Task-Adaptive Embedding Learning (TAEL) framework that complements the existing methods by giving full play to feature embedding’s dual roles in few-shot scene classification - representing images and constructing classifiers in the embedding space. First, we design a lightweight network that enriches the diversity and expressive capacity of embeddings by dynamically fusing information from multiple kernels. Second, we present a task-adaptive strategy that helps to generate more discriminative representations by transforming the universal embeddings into task-specific embeddings via a self-attention mechanism. We evaluate our model in the standard few-shot learning setting on two challenging datasets: NWPU-RESISC4 and RSD46-WHU. Experimental results demonstrate that, on all tasks, our method achieves state-of-the-art performance by a significant margin.

Detecting Multielement Algorithmically Generated Domain Names Based on Adaptive Embedding Model

With the development of detection algorithms on malicious dynamic domain names, domain generation algorithms have developed to be more stealthy. The use of multiple elements for generating domains will lead to higher detection difficulty. To effectively improve the detection accuracy of algorithmically generated domain names based on multiple elements, a domain name syntax model is proposed, which analyzes the multiple elements in domain names and their syntactic relationship, and an adaptive embedding method is proposed to achieve effective element parsing of domain names. A parallel convolutional model based on the feature selection module combined with an improved dynamic loss function based on curriculum learning is proposed, which can achieve effective detection on multielement malicious domain names. A series of experiments are designed and the proposed model is compared with five previous algorithms. The experimental results denote that the detection accuracy of the proposed model for multiple-element malicious domain names is significantly higher than that of the comparison algorithms and also has good adaptability to other types of malicious domain names.

INFLUENCE OF DIGITAL IMAGES PRELIMINARY NOISING ON STATISTICAL STEGDETECTORS PERFORMANCE

Context. The problem of sensitive information protection during data transmission in communication systems was considered. The case of reliable detection of stego images formed according to advanced embedding methods was investigated. The object of research is digital images steganalysis of adaptive steganographic methods. Objective. The goal of the work is performance analysis of statistical stegdetectors for adaptive embedding methods in case of preliminary noising of analyzed image with thermal and shot noises. Method. The image pre-processing (calibration) method was proposed for improving stego-to-cover ratio for state-of-the-art adaptive embedding methods HUGO, MG and MiPOD. The method is aimed at amplifying negligible changes of cover image caused by message hiding with usage of Gaussian and Poisson noises. The former one is related to influence the thermal noise of chargecoupled device (CCD) based image sensor during data acquisition. The latter one is related to shot noise that originates from stochastic process of electron emission by photons hitting of CCD elements. During the research, parameters of thermal noise were estimated with two-dimensional Wiener filter, while sliding window of size 5·5 pixels was used for parameters evaluation for shot noise. Results. The dependencies of detection error on cover image payload for advance HUGO, MG and MiPOD embedding methods were obtained. The results were presented for the case of image pre-noising with both Gaussian and Poisson noises, and varying of feature pre-processing methods. Conclusions. The conducted experiments confirmed effectiveness of proposed approach for image calibration with Poisson noise. Obtained results allow us to recommend linearly transformed features to be used for improving stegdetector performance by natural image processing. The prospects for further research may include investigation usage of special noises, such as fractal noises, for improving stego-to-cover ratio for advanced embedding methods.