Adversarial shared‐private networks for specific emitter identification

Electronics LettersVolume 56, Issue 6 p. 296-299 Information and communicationsFree Access Adversarial shared-private networks for specific emitter identification Peibo Chen, Corresponding Author Peibo Chen chenpeibo13@nudt.edu.cn College of Electronic Science, National University of Defense Technology, Changsha, People's Republic of ChinaSearch for more papers by this authorYulan Guo, Yulan Guo College of Electronic Science, National University of Defense Technology, Changsha, People's Republic of ChinaSearch for more papers by this authorGang Li, Gang Li College of Electronic Science, National University of Defense Technology, Changsha, People's Republic of ChinaSearch for more papers by this authorJianwei Wan, Jianwei Wan College of Electronic Science, National University of Defense Technology, Changsha, People's Republic of ChinaSearch for more papers by this author Peibo Chen, Corresponding Author Peibo Chen chenpeibo13@nudt.edu.cn College of Electronic Science, National University of Defense Technology, Changsha, People's Republic of ChinaSearch for more papers by this authorYulan Guo, Yulan Guo College of Electronic Science, National University of Defense Technology, Changsha, People's Republic of ChinaSearch for more papers by this authorGang Li, Gang Li College of Electronic Science, National University of Defense Technology, Changsha, People's Republic of ChinaSearch for more papers by this authorJianwei Wan, Jianwei Wan College of Electronic Science, National University of Defense Technology, Changsha, People's Republic of ChinaSearch for more papers by this author First published: 01 March 2020 https://doi.org/10.1049/el.2019.3207Citations: 4AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Deep learning provides a new scheme for specific emitter identification (SEI). However, most deep learning-based methods directly apply existing neural networks in other fields to SEI without considering its particularity. The identification-relevant information only accounts for a small proportion of the information carried by the received signal, and thus is easily interfered by the rest identification-irrelevant information during its mining process. This Letter proposes adversarial shared-private networks (ASPN) to address the most challenging case where all emitters are of the same type. By separating the normalised signal bispectrum amplitude and phase in the first quadrant into two orthogonal components, one that is shared among all emitters and the other that is private to each emitter, ASPN extracts more effective features and achieves more remarkable performances than most methods in the literature. Introduction Specific emitter identification (SEI) aims to precisely identify individual emitters using the fingerprint features, which represent the differences among all emitters. For the most challenging case where all emitters are of the same type, the differences originated from the unique hardware imperfections are extremely subtle. Therefore, it is crucial to mine these differences hidden in the received signal during the feature extraction. Existing feature extraction schemes can be divided into manual and automatic ones. The manual scheme extracts handcrafted features by the present expertise. Examples of this scheme include wavelet transform-based method [1], Hilbert–Huang transform-based methods [2, 3] and bispectrum transform-based methods [4-6]. In recent years, the automatic scheme based on deep learning becomes increasingly attractive. Deep features extracted by classical neural networks, such as deep autoencoders [7], long short-term memory [8] and convolutional neural networks (CNN) [9-11], are more discriminative than most handcrafted features. However, these deep learning-based methods pay little attention to our particularity. Specifically, the information carried by the received signal can be divided into two parts: common information which is shared among all emitters and individual information which is private to each emitter. Although useful for our identification, the individual information is very weak and imperceptible compared to the harmful common information. The major limitation of existing neural networks is that, they do not have a clear mechanism to guarantee that the extracted features contain only individual information and no common information. So these neural networks hardly make significant breakthroughs in our identification. Overall idea To adequately take advantage of deep learning for the identification of the same type of emitters, we propose adversarial shared-private networks (ASPN). The identification flowchart of ASPN is depicted in Fig. 1. We first transform the received signal into the bispectrum, a three-order spectrum widely used in SEI [4-6]. Then, considering the certain symmetry of the bispectrum and the irrelevance between the bispectrum amplitude and phase (BAP) [6], the normalised BAP in the first quadrant is defined as the two-channel input of ASPN. After this preprocessing, the input is separated by ASPN into two sets of features: shared features and private features. The former contain common information only, and the latter contain the individual information only. More importantly, ASPN ensures that the shared features and the private features do not intersect each other. Consequently, the extracted private features according to the separation mechanism are very effective for our identification. Fig 1Open in figure viewerPowerPoint Identification flowchart of ASPN for SEI Overall architecture The dual-model architecture of ASPN is illustrated in Fig. 2. We have two extractor-classifier structured neural networks, a shared model and a private model. Although their convolutional extractors mirror each other and their fully-connected classifiers also mirror each other, the purposes of these two models are opposed. The shared model is designed to extract the shared features rather than the private features, which is the goal of the private model. To meet these expectations, we introduce two strategies: adversarial training and an orthogonality constraint (OC). Specifically, a gradient reversal layer (GRL) [12] is inserted into the shared model to perform the adversarial training between the shared extractor and the shared classifier, while OC is used to connect the shared features and the private features and thus prevent them from interfering with each other. Fig 2Open in figure viewerPowerPoint Dual-model architecture of ASPN for SEI More formally, a training dataset is represented as , where is an input and is the one-hot vector encoding the class label of . First, is fed into the shared extractor SE (parameterised by ) and the private extractor PE (parameterised by ) simultaneously, resulting in a shared feature vector and a private feature vector respectively. Second, the shared classifier SC (parameterised by ) estimates the purity of with the assistance of GRL, while the private classifier PC (parameterised by ) predicts the class of . Besides, we add OC to eliminate the redundancy between and . The details about these models and strategies are discussed in the following sections. Adversarial training The shared model aims to extract that contains more common information without any individual information. However, minimising the cost function between the one-hot vectors of inputs and the softmax outputs of the shared model, which only increases the proportion of the individual information in , hardly satisfies this requirement. Inspired by generative adversarial nets [13], we introduce adversarial training into the shared model for encouraging to be more pure for all emitters. The adversarial training between SE and SC is essentially a two-player zero-sum game. During the training phase, SE is optimised to capture more common information for misleading SC, whereas SC is learned to make more correct predictions based on this common information. After the training phase, SE and SC reach a Nash equilibrium at which they both cannot improve any more. As a result, much common information is mined, but some individual information is still not discarded. The adversarial training is usually considered as a complex min–max optimisation: (1)where CE denotes the cross entropy. The saddle point in (1) can only be searched using the alternating stochastic gradient updates. Note that the private model and OC also need to be trained together with the shared model, so it is inconvenient to perform the adversarial training by the min–max optimisation. Fortunately, the optimisation form can be simplified by GRL, a special layer without any parameters. During the forward propagation, GRL works as an identity transformation. During the backpropagation, GRL takes the gradient from the subsequent level and changes its sign before passing it to the preceding layer. We can formally treat GRL as a pseudo function R. Its forward propagation is described as: (2)while the backpropagation is described as: (3)where is the identity matrix. Once GRL is inserted between SE and SC, the adversarial training can be transformed from the min–max optimisation to a convenient minimisation problem: (4)where denotes the cost function of the shared model. As the backpropagation process passes through GRL, the partial derivatives of the downstream with respect to the upstream get multiplied by (i.e. is replaced with ). Therefore, directly running the stochastic gradient descent or its variants in the resulting shared model can converge to the same saddle point in (1). Orthogonality constraint The private model aims to extract that contains more individual information without any common information, and then make a correct prediction based on . The conventional solution to this requirement is to minimise the cost function between the one-hot vectors of inputs and the softmax outputs of the private model, as follows: (5)where denotes the cost function of the private model. Although the dual-model architecture with the adversarial training encodes as and which both contain rich information they need, there is no guarantee that some individual information is not stuck in and is not contaminated by some common information. To solve this problem, a constraint is needed to force SE and PE to work in two disjoint or even orthogonal spaces. In other words, the redundancy between and should be penalised as much as possible by a certain constraint. After exploring several optional constraints, we find that minimising OC used in [14] is enough to eliminate their overlap: (6)where denotes the cost function of OC and is the Frobenius norm. and are two feature matrices in which the ith rows are and , respectively. In summary, the training of ASPN is to optimise the objective function with respect to all parameters : (7)where and are weights ranging from 0 to 1. Experiments In this section, we test the performance of ASPN, compared against two bispectrum transform-based methods [11, 6] with different schemes. One is a CNN trained on a compressed bispectrum (CB-CNN), and the other is a BAP-based handcrafted feature extraction (BAP-features). Additionally, we also investigate the impact of OC by setting to 0 (ASPN w/o OC). The two real datasets were acquired by two groups of emitters with different types. Each of these two datasets has 8 emitters of the same type, while each emitter contains 60,000 training samples and 20,000 test samples. The layouts of the shared model in these two experiments are shown in Figs. 3 and 4, respectively. According to the design of VGG [15], each layout consists of a sequence of convolutional modules and a fully-connected layer, followed by GRL and several fully-connected layers. The upstream of GRL is the shared extractor, in which each convolutional module is composed of one or two convolutional layers and a max pooling layer. The downstream of GRL is the shared classifier. The only difference between these two layouts is that each convolutional module in Fig. 4 contains one more convolutional layer than that in Fig. 3. For the private model, its layouts are the same as those of the shared model without GRL. Additionally, the values of in the two experiments are and . The Adam optimiser with a learning rate of 0.001 is selected as the optimisation algorithm of (7). Fig 3Open in figure viewerPowerPoint Shared model layout of ASPN in the first experiment Fig 4Open in figure viewerPowerPoint Shared model layout of ASPN in the second experiment We repeated each method 5 times on each dataset and present the average identification accuracy in Table 1. ASPN achieves the best overall performance in all experiments. Compared to CB-CNN and BAP-features, ASPN outperforms each of them with a gap of over 10% in each experiment. Note that ASPN w/o OC with the worst results is essentially a standard CNN (the private model) similar to CB-CNN. These imply that the success of ASPN depends on the close cooperation between the two models rather than a single model (CB-CNN or the private model). Therefore, ASPN offers a general architecture that takes less effort on the layout, compared to other deep learning-based methods. Table 1. Average identification accuracy of the four methods in the two experiments Method First experiment, % Second experiment, % ASPN 99.96 99.68 CB-CNN 88.44 87.59 BAP-features 81.84 77.97 ASPN w/o OC 56.59 57.54 Besides the quantitative results, we also visualise the two kinds of high-dimensional feature vectors extracted by ASPN in a 2D plane using t-SNE [16]. We randomly select 2400 test samples from each dataset and show the resulting visualisations in Figs. 5 and 6, respectively. In each figure, these shared feature vectors are indicated by various light-colored points, while these private feature vectors are indicated by various dark-coloured points. The class labels (1 to 8) and initials (s and p) of these feature vectors are also provided in the two figures. Fig 5Open in figure viewerPowerPoint Visualisation of the shared features and the private features in the first experiment Fig 6Open in figure viewerPowerPoint Visualisation of the shared features and the private features in the second experiment Each figure is divided into two parts: a shared feature subspace and a private feature subspace. In the shared feature subspace, all shared feature vectors are uniformly distributed on the same straight line. In the private feature subspace, all private feature vectors are obviously clustered into eight groups with high intra-class cohesion and large inter-class dispersion. Moreover, from the distribution areas occupied by these shared feature vectors and private feature vectors of each emitter, it is true that the proportion of the individual information in the received signal is indeed much smaller than that of the common information. Conclusion In this Letter, we have proposed ASPN for the identification of the same type of emitters. 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