A New Multi-Scale Convolutional Model Based on Multiple Attention for Image Classification

Zhao

Security and Communication Networks

et al. 2021

With the rapid increase in the amount and type of malware, traditional methods of malware detection and family classification for IoT applications through static and dynamic analysis have been greatly challenged. In this paper, a new simple and effective attention module of Convolutional Neural Networks (CNNs), named as Depthwise Efficient Attention Module (DEAM), is proposed and combined with a DenseNet to propose a new malware detection and family classification model. Based on the good effect of the DenseNet in the field of image classification and the visual similarity of the malware family on images, the gray-scale image transformed from malware is input into the model combined with the DEAM and DenseNet for malware detection, and then the family classification is carried out. The DEAM is a general lightweight attention module improved based on the Convolutional Block Attention Module (CBAM), which can strengthen the attention to the characteristics of malware and improve the model effect. We use the MalImg dataset, Microsoft malware classification challenge dataset (BIG 2015), and our dataset constructed by the two above-mentioned datasets to verify the effectiveness of the proposed model in family classification and malware detection. Experimental results show that the proposed model achieves 99.3% in terms of accuracy for malware detection on our dataset and achieves 98.5% and 97.3% in terms of accuracy for family classification on the MalImg dataset and BIG 2015 dataset, respectively. The model can reliably detect IoT malware and classify its families.

Section: Attention Mechanismmentioning

confidence: 99%

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

Zhao

Security and Communication Networks

et al. 2021

“…Res2Net randomly grouping residual blocks will affect the robustness and intuitive interpretability of the network. AMS-CNN [41] introduces channel sorting and grouping convolution, which makes more important feature channels perform more convolution operations to achieve multi-scale expression. Next, we use the improved channel expansion algorithm to construct the object-level attention multi-scale convolution model in AMS-CNN, and implement the object-level attention based on the channel expansion strategy in the intra-layer multi-scale convolution.…”

Section: B Multi-scale Convolutional Modelmentioning

confidence: 99%

“…As shown in Figure 1, the "channel sorting module" is first introduced, and the "channel expansion module" will be described in detail later. Here, "channel sorting module [41]" is used to obtain channel importance vector U , where U is a one-dimensional vector that records the importance of each feature channel. The implementation of the channel-sorting strategy can be divided into two parts: first, the global average pooling method is directly used to obtain the feature average value of each channel to represent the importance of the channel; second, the channel-by-channel convolution method is adopted, that is, the global depth-wise convolution, in which the sizes of the convolution kernels and the scales of the feature channels are the same.…”

Section: A Channel Expansion Networkmentioning

confidence: 99%

Channel Expansion Convolutional Network for Image Classification

2020

Self Cite

With the continuous evolution of research on convolutional neural networks, it is an efficient and fashionable method to introduce attention mechanism into the convolutional structure. The channel attention designed in SENet has made a great contribution to the promotion of the attention convolution model. However, our research found that SENet focuses on certain feature channels rather than objects in the channels. It will simultaneously enhance or weaken the target objects and background information in a certain channel. On the basis of the channel attention convolution network, we first perform channel sorting and group convolution on the feature map, and expand each group to β times the original feature channel during the group convolution process to construct a channel expansion convolution network (CENet), where β is an array used to represent the channel expansion coefficient. CENet captures the attention of objects in the feature channel while expanding the proportion of features in the relatively important channel. Furthermore, we improved the structure of CENet and merged it into the intra-layer multi-scale convolutional model to construct an object-level attention multi-scale convolutional neural network (OAMS-CNN). We have conducted a large number of experiments on four data sets, CIFAR-10, CIFAR-100, FGVC-Aircraft and Stanford Cars. The experimental results show that our proposed new object-level attention convolution model has achieved good image classification results. INDEX TERMS Channel expansion network, object-level attention CNN, multi-scale CNN, image classification.

“…ISN provides the possibility of segmenting the adhesive characters, we used the same idea in this work. Attention mechanism: Attention mechanism are widely used in computer vision [28,29] and natural language processing [30]. X. Wang et al [31] proposed a non-local network for video classification, which is based on a spacetime dependency attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

TSER: A Two-Stage Character Segmentation Network With Two-Stream Attention and Edge Refinement

et al. 2020

Segmenting characters in an image is a classic yet challenging task in computer vision. Correctly determining boundaries of adhesive characters with various scales and shapes is essential for character segmentation, especially for separating handwritten characters. Nevertheless, there is seldom work in the literature which can achieve satisfactory performance. In this article, by leveraging the ability of deep neural networks, we proposed a two-stage character segmentation network with two-stream attention and edge refinement (TSER) to tackle this problem. TSER firstly locates every character by object detection, then extracts their corresponding contours. In the process, a novel two-stream attention mechanism (TSAM) is proposed to make the network focus more on the discrepancy of character boundaries. Furthermore, a novel generating method is used to dynamically generate anchors on different feature levels to improve model's sensitivity on the shapes and scales of characters. Eventually a cascaded edge refinement network is used to obtain contour of each character. To prove the efficiency and generalization ability of our model, we compared TSER with traditional algorithms and other deep learning models on two commonly used datasets in different segmentation tasks. The comparative result indicated that TSER reached state-of-the-art performance.