Energy disaggregation is a pivotal technique employed in Non-Intrusive Load Monitoring (NILM) to accurately estimate the power consumption of individual appliances in households. Recent advancements in trasformer models, particularly in domains like Natural Language Processing (NLP), have underscored their potential for enhancing classification performance. In this paper, we propose a novel algorithm for energy decomposition using an adapted transformer-based architecture. Our approach integrates the rapid Fourier transform with a multi-head attention mechanism to efficiently transform time-series feature data into frequency domain features, followed by noise reduction achieved by multiplying the data with a trainable matrix. The DFT-NILM model, also known as the denoising Fourier transformer, has been evaluated on publicly available electrical data. The evaluation shows that the DFT-NILM model outperforms existing state-of-the-art methods in terms of performance.
Some tracking algorithms based on Siamese network have made great progress in similarity learning via features cross-correlation between an object branch and a search branch. However, it is significantly challenging for object tracking in video sequences in terms of target deformation with greatly varying. We propose a Siamese network based on global and local feature matching for object tracking including three phases with the aim of addressing the above issues. In the first phase, obtaining the global similarity matching and local relational mapping similarity of the template branch and the search branch by a selection mechanism of object template-aware features are to reduce the impact of background features on the local matching. In the second phase, introducing correlation matching of the local feature for establishing correspondence among partial-level pixels. Finally, combining the classification and regression results with global matching features and local matching features in a weighted fusion. Extensive experiments are conducted on datasets (OTB-100, LaSOT and GOT-10K) demonstrate that the proposed network enables to achieve superiority compared against the state-of-the-art method and provides an efficient scenario for tackling the issue.
The purpose of image steganalysis is to detect whether the secret information is hidden in the image. The current advanced adaptive steganography algorithm hides the secret information in the complex area of the image texture. However, it is difficult for the image steganalysis model to capture enough noise residual, which makes the detection ability of the model insufficient. To further improve the detection ability of the spatial image steganalysis model, a U-Net-based auxiliary information generation network is first constructed to increase the size of noise residual in the image so that the model can capture more favorable information. Besides, the spatial and channel attention mechanisms are fused to guide the model to pay more attention to the regions of the image that are globally favorable for steganalysis. To verify the effectiveness of the proposed model, experiments are conducted on the BOSSbase-v1.01 dataset through advanced spatial adaptive steganography algorithms S-UNIWARD and WOW. The experimental results show that the proposed model can improve the detection accuracy by up to 4.5% compared with the current optimal deep learning-based spatial image steganalysis models SR-Net and Siamese-Net.
As an effective method of information hiding, steganography embeds secret information into images in a way that is not perceived by humans. Recent interest in the combination of image steganography and generative adversarial networks (GANs) has yielded rapid progress. However, existing steganography frameworks still suffer from the low quality of the steganographic images and weak resistance to detection by steganalysis algorithms. To overcome these limitations, we propose an effective GAN-based image steganography framework with multiscale features integration. Specifically, we construct the secret image feature extraction network (SfeNet), which is driven by the spatial attention mechanism to extract multiscale features of secret images. And the encoder combined with the efficient channel attention mechanism is presented to embed multiscale features of secret images into the cover image. Subsequently, a steganalyzer is incorporated as the discriminator with the encoder in GAN to strengthen the ability of the model to resist steganalysis. Besides, a mixed loss function is proposed by combining perceptual loss, MS-SSIM, and L1 loss to preserve the structural similarity of images. Experimental results on ImageNet, Pascal VOC2012, and LFW show that the proposed method achieves better quality steganographic images and better resistance to steganalysis compared to some of the state-of-the-art steganography algorithms.
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