随着深度学习技术的飞速发展,AI伪造图像技术日益成熟,对电子取证工作带来了严峻挑战。为应对这一威胁,在深入调研深度伪造技术和图像检测理论的基础上,提出基于ResNeXt网络的深度伪造人脸图像检测模型,并将其应用于电子取证领域。深入...随着深度学习技术的飞速发展,AI伪造图像技术日益成熟,对电子取证工作带来了严峻挑战。为应对这一威胁,在深入调研深度伪造技术和图像检测理论的基础上,提出基于ResNeXt网络的深度伪造人脸图像检测模型,并将其应用于电子取证领域。深入剖析了ResNeXt网络在特征提取方面的优势。针对深度伪造人脸图像的独特性,设计了一个多尺度特征融合模块,以有效捕捉伪造图像中细微的伪造痕迹。通过在实际电子取证场景中的应用验证,该方法在FaceDB数据集上取得了显著效果,准确率为88%,曲线下面积(Area Under Curve,AUC)达到0.89,为电子取证提供了强有力的技术支持。展开更多
To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network mo...To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.展开更多
针对风机滚动轴承故障诊断需要提取大量复杂特征,提出一种基于注意力机制、ResNext网络和长短时记忆(Long Short Term Memory,LSTM)网络的并行轴承故障诊断模型。首先,将采集的一维振动信号进行预处理;然后,分两路输入到模型中提取特征...针对风机滚动轴承故障诊断需要提取大量复杂特征,提出一种基于注意力机制、ResNext网络和长短时记忆(Long Short Term Memory,LSTM)网络的并行轴承故障诊断模型。首先,将采集的一维振动信号进行预处理;然后,分两路输入到模型中提取特征,其中一路输入到嵌入注意力机制的ResNext模块中,注意力机制可以增加重要特征的权重,减少模型运算量,另一路输入到LSTM网络中提取振动信号在时间序列上的依赖关系;最后,将两路提取到的特征进行融合输入到Softmax层进行故障分类。实验结果表明,与目前基于深度学习的轴承故障诊断方法相比,所提方法在轴承故障分类准确率上表现更好。展开更多
文摘随着深度学习技术的飞速发展,AI伪造图像技术日益成熟,对电子取证工作带来了严峻挑战。为应对这一威胁,在深入调研深度伪造技术和图像检测理论的基础上,提出基于ResNeXt网络的深度伪造人脸图像检测模型,并将其应用于电子取证领域。深入剖析了ResNeXt网络在特征提取方面的优势。针对深度伪造人脸图像的独特性,设计了一个多尺度特征融合模块,以有效捕捉伪造图像中细微的伪造痕迹。通过在实际电子取证场景中的应用验证,该方法在FaceDB数据集上取得了显著效果,准确率为88%,曲线下面积(Area Under Curve,AUC)达到0.89,为电子取证提供了强有力的技术支持。
基金supported by National Natural Science Foundation of China(No.61862037)Lanzhou Jiaotong University Tianyou Innovation Team Project(No.TY202002)。
文摘To solve the problems of redundant feature information,the insignificant difference in feature representation,and low recognition accuracy of the fine-grained image,based on the ResNeXt50 model,an MSFResNet network model is proposed by fusing multi-scale feature information.Firstly,a multi-scale feature extraction module is designed to obtain multi-scale information on feature images by using different scales of convolution kernels.Meanwhile,the channel attention mechanism is used to increase the global information acquisition of the network.Secondly,the feature images processed by the multi-scale feature extraction module are fused with the deep feature images through short links to guide the full learning of the network,thus reducing the loss of texture details of the deep network feature images,and improving network generalization ability and recognition accuracy.Finally,the validity of the MSFResNet model is verified using public datasets and applied to wild mushroom identification.Experimental results show that compared with ResNeXt50 network model,the accuracy of the MSFResNet model is improved by 6.01%on the FGVC-Aircraft common dataset.It achieves 99.13%classification accuracy on the wild mushroom dataset,which is 0.47%higher than ResNeXt50.Furthermore,the experimental results of the thermal map show that the MSFResNet model significantly reduces the interference of background information,making the network focus on the location of the main body of wild mushroom,which can effectively improve the accuracy of wild mushroom identification.
文摘针对风机滚动轴承故障诊断需要提取大量复杂特征,提出一种基于注意力机制、ResNext网络和长短时记忆(Long Short Term Memory,LSTM)网络的并行轴承故障诊断模型。首先,将采集的一维振动信号进行预处理;然后,分两路输入到模型中提取特征,其中一路输入到嵌入注意力机制的ResNext模块中,注意力机制可以增加重要特征的权重,减少模型运算量,另一路输入到LSTM网络中提取振动信号在时间序列上的依赖关系;最后,将两路提取到的特征进行融合输入到Softmax层进行故障分类。实验结果表明,与目前基于深度学习的轴承故障诊断方法相比,所提方法在轴承故障分类准确率上表现更好。
