Discriminative adversarial networks for specific emitter identification

Electronics LettersVolume 56, Issue 9 p. 438-441 Information and communicationsFree Access Discriminative adversarial 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 China Yulan Guo: Also with School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou, 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 authorLing Wang, Ling Wang 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 China Yulan Guo: Also with School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou, 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 authorLing Wang, Ling Wang 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 April 2020 https://doi.org/10.1049/el.2020.0116Citations: 8 AboutSectionsPDF 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 The crucial issue in specific emitter identification (SEI) is the extraction of fingerprint features which can represent the differences among individual emitters of the same type. Considering that these emitters have the same intentional modulation on pulse, the fingerprint features originated from the unintentional modulation on pulse are extremely imperceptible and less detectable. However, existing feature extractions, either traditional handcrafted ones or deep learning based ones, have failed to ensure that their extracted features are rich in the unintentional modulation information (UMI) and not interfered by the intentional modulation information (IMI). To adequately take advantage of deep learning to address SEI, this Letter proposes a novel neural networks, named discriminative adversarial networks (DAN). By demarcating a clear boundary between IMI and UMI, DAN isolates IMI and thus reduces the burden of UMI mining during its feature extraction process. Experimental results demonstrate that DAN outperforms most methods in the literature. Introduction Specific emitter identification (SEI), also known as emitter fingerprint identification, is a technique to identify individual emitters of the same type by extracting fingerprint features. Existing feature extractions can be roughly divided into traditional handcrafted ones and deep learning based ones. Handcrafted extractions with the present expertise include wavelet transform [1], Hilbert–Huang transform [2], and bispectrum transform [3]. In recent years, deep learning becomes increasingly attractive in SEI. Classical neural networks, such as long short-term memory [4] and convolutional neural networks (CNN) [5, 6], have been investigated to extract features. However, these methods have never paid much attention to the fact that fingerprint features produced by the unintentional modulation on pulse are extremely imperceptible and less detectable when all emitters are of the same intentional modulation on pulse. In other words, both handcrafted extractions and deep learning based extractions have failed to prevent the unintentional modulation information (UMI) mining from being interfered by the intentional modulation information (IMI) during their feature extraction processes. As shown in Fig. 1, the united information space (rectangle in the left) of two emitters A (solid square) and B (dotted square) is divided into three parts, a common part (middle in the rectangle) which is shared between these two emitters, and two individual parts (rightmost and leftmost in the rectangle) which are private to their respective emitters. The common part contains all IMI (yellow circles) on both emitters, whereas each individual part contains only UMI (green triangles) carried by its corresponding emitter. Considering that these two emitters are of the same type, the size of the common part is much larger than that of each individual part. However, existing methods cover both the common and individual parts of each emitter, resulting in that their extracted features not only contain much useful UMI but also are contaminated by some harmful IMI. Fig 1Open in figure viewerPowerPoint Two different fingerprint feature extraction schemes for SEI It is better to limit an extraction method to these individual parts. Only by this way can its extracted fingerprint features be rich in UMI and poor in IMI. According to this scheme, we propose a novel neural network, named discriminative adversarial network (DAN). DAN correctly locates its working area by demarcating a clear boundary (red circle in Fig. 1) between the common part and these individual parts. This boundary isolates IMI and thus reduces the burden of UMI mining. As a result, its extracted features are more effective than most features in the literature. Discriminative adversarial networks Existing deep learning based extractions usually set various two-dimensional transforms of the received signal as their respective input representations. A normal CNN is then used to extract fingerprint features from these transforms [5, 6]. Following this identification process, we first estimate the signal bispectrum, a three-order spectrum, which has been widely applied in SEI [3, 6]. According to its certain symmetry and the irrelevance between its amplitude and phase (BAP) [3], we then define the normalised BAP in the first quadrant as the two-channel input of DAN. Furthermore, DAN introduces feature factorisation, adversarial training and a discriminator into an extractor-classifier structured CNN (as shown in Fig. 2), enabling this CNN to demarcate a clear boundary between IMI and UMI. Fig 2Open in figure viewerPowerPoint Architecture of DAN Specifically, feature factorisation is used to split the latent features output by the extractor into shared features and private features (yellow and green in Fig. 2). To make the shared features and the private features rich in IMI and UMI, respectively, a gradient reversal layer (GRL) [7] (red in Fig. 2) is inserted between the shared features and the classifier. By simplifying adversarial training between the extractor and the classifier, GRL forces the extractor to capture IMI for misleading the classifier. Therefore, more IMI is isolated in the shared features, while more UMI excluded by GRL is easily stored in the private features using conventional minimisation about the extractor and the classifier. However, feature factorisation and adversarial training are insufficient to avoid that a small amount of IMI and UMI remain in the private features and the shared features, respectively. To correctly place these residual IMI and UMI, a discriminator (blue in Fig. 2) is added after the extractor. As a binary classifier whose last softmax layer has only two neurones, the task of the discriminator is to classify these two types of features as 'shared' or 'private' rather than their categories. Therefore, the boundary between IMI and UMI becomes clearer and stronger. More formally, given a training dataset , where is an input and is the corresponding category one-hot vector. The extractor E, the classifier C and the discriminator D are parameterised by , and , respectively. First, is fed into E and then split into a shared feature vector and a private feature vector , whose one-hot vectors and encoding the binary labels 'shared' and 'private' are also provided. Secondly, and are passed to C and D simultaneously, resulting in two softmax output vectors and for their category labels, and other two softmax output vectors and for their binary labels. The training of DAN is to minimise the following loss with respect to all parameters : (1)where and are weights ranging from 0 to 1. To extract with more UMI, we conventionally define the private loss as the sum of the cross entropy between all pairs of and : (2)where CE denotes the cross entropy and . Inspired by generative adversarial nets [8], we force to hold more IMI using adversarial training between E and C. Adversarial training is essentially a zero-sum game between two players, in which E is rewarded for mining IMI that did fool C and C is updated for correct predictions based on this IMI. This cycle continues until E and C reach an equilibrium at which they both cannot improve any more. As a result, a large amount of IMI is isolated in . However, adversarial training is usually implemented by a complex min–max problem: (3)where . To formalise this game as a convenient minimisation problem, we introduce GRL, which has no parameters associated with it. During the forward propagation, GRL acts as an identity transformation. During the backpropagation, GRL takes the gradient from the subsequent level, and then changes its sign before passing it to the preceding layer. Mathematically, we treat GRL as a pseudo function R defined by two equations describing its forward propagation and backpropagation behaviours (4) (5)where is the identity matrix. Therefore, adversarial training can be simplified to the minimisation of the shared loss : (6)where . To repatriate these UMI and IMI hiding in and , respectively, we design the discriminative loss as follows: (7)where and . The minimisation of the first sum term sends UMI that is stuck in back to , while that of the second sum term prevents from being infiltrated by IMI. Therefore, with rich UMI but without any IMI is ideally competent for SEI. Performance evaluation We evaluate the performance of DAN by conducting experiments on two real datasets acquired from two groups of emitters with different types. Each dataset comprises 8 emitters of the same type, with 60,000 training samples and 20,000 test samples per emitter. Two comparison methods are also based on the bispectrum transform: one is a normal CNN trained on a compressed bispectrum (CB-CNN) [6], and the other is a BAP-based handcrafted extraction (BAP-features) [3]. We also analyse adversarial training by removing GRL and multiplying by (DAN/AT), and the discriminator by setting to 0 (DAN/DIS). Additionally, the discriminator is further investigated by replacing it with the soft subspace orthogonality constraint [9] (DAN-OC): (8)where is the Frobenius norm. and are matrices whose the ith rows are and , respectively. The layout of DAN in the first experiment adopts the design scheme of VGG [10], as shown in Fig. 3. It is made up of three main components: the convolutional extractor (left), the fully-connected classifier (upper right) and the fully-connected discriminator (lower right). The extractor consists of a sequence of convolutional modules and a fully-connected layer, followed by feature factorisation and GRL. As the main element of the extractor, each convolutional module is composed of a convolutional layer and a max pooling layer. The discriminator mirrors the classifier except for its last softmax layer. The DAN in the second experiment has a similar layout, except that its each convolutional module contains one more convolutional layer than that in Fig. 3. Besides, the values of in these two experiments are and . The optimiser is the Adam algorithm with a learning rate of 0.001. Fig 3Open in figure viewerPowerPoint Layout of DAN in the first experiment Experimental results obtained by averaging five repeated runs of each method on each dataset are presented in Table 1. Compared to CB-CNN and BAP-features, DAN significantly outperforms each of them with a gap of over 10% in each experiment. DAN/AT and DAN/DIS perform less well than CB-CNN and BAP-features, not to mention DAN. Although slightly inferior to DAN, DAN-OC also yields remarkable results. Table 1. Average identification accuracy of the six methods in the two experiments Method First experiment, % Second experiment, % DAN 99.99 99.82 CB-CNN 88.44 87.59 BAP-features 81.84 77.97 DAN/AT 12.50 12.50 DAN/DIS 53.61 55.65 DAN-OC 99.98 99.75 The maximisation of (multiplying by ) can indeed replace adversarial training (removing GRL) to mine IMI, but the resulting unbounded increase of weakens the contributions of and in the whole optimisation process. So only by adversarial training can DAN extract the shared features without hindering other losses. Note that, the removal of (setting to 0) turns DAN into a normal CNN with the poor performance. It implies that the private features purified by the discriminator are more effective than these features extracted by a normal CNN without the discriminator. To further compare the discriminator and the orthogonality constraint, we visualise the high-dimensional feature vectors extracted by DAN and DAN-OC as two-dimensional points using t-SNE [11]. Specifically, we randomly select 2400 samples from each dataset and show resulting visualisations in Figs. 4-7. The light-coloured points correspond to these shared feature vectors, while the dark-coloured points correspond to these private feature vectors. Besides, the category labels (1 to 8) and binary labels (s and p) of these feature vectors are also provided in these figures. Fig 4Open in figure viewerPowerPoint Feature visualisation of DAN in the first experiment Fig 5Open in figure viewerPowerPoint Feature visualisation of DAN-OC in the first experiment Fig 6Open in figure viewerPowerPoint Feature visualisation of DAN in the second experiment Fig 7Open in figure viewerPowerPoint Feature visualisation of DAN-OC in the second experiment Obviously, each figure is divided into two disjoint parts by the discriminator or the orthogonality constraint. All shared points are gathered in the lower right part, while all private points in the upper left part are clustered into eight groups. Despite these shared points are more compact, the strict orthogonality constraint causes that these private points have smaller inter-class dispersion and a complex circular distribution (Figs. 5 and 7). Conversely, the discriminator not only transforms the distribution of these private points into an easily identifiable linear form with larger inter-class dispersion, but also further reflects the similarity degree between each pair of emitters through the unbalanced cohesion of these shared points (Figs. 4 and 6). Therefore, it is more reasonable to select the discriminator as the boundary demarcation criterion. Conclusion This Letter has proposed DAN for SEI. DAN arms a normal CNN with a clear boundary between IMI and UMI by introducing feature factorisation, adversarial training and a discriminator. The feature factorisation and adversarial training specify the information types on both sides of the boundary, while the discriminator strengthens the boundary to prevent these two types of information from interfering with each other. Therefore, our proposed model ensures that the extracted private features contain rich UMI without any IMI. We evaluated DAN on two real datasets using both quantitative and qualitative analyses, with state-of-the-art results being achieved. Acknowledgments This work was supported by the National Natural Science Foundation of China (grant nos. 61602499, 61972435), and the Fundamental Research Funds for the Central Universities (grant no. 18lgzd06). References 1Bertoncini, C., Rudd, K., Nousain, B. et. al.,: 'Wavelet fingerprinting of radio-frequency identification (RFID) tags', IEEE Trans. Ind. Electron., 2012, 59, (12), pp. 4843– 4850 (https://doi/org/10.1109/TIE.2011.2179276) 2Zhang, J., Wang, F., Dobre, O.A. et. al.,: 'Specific emitter identification via Hilbert–Huang transform in single-hop and relaying scenarios', IEEE Trans. Inf. Forensics Sec., 2016, 11, (6), pp. 1192– 1205 (https://doi/org/10.1109/TIFS.2016.2520908) 3Han, J., Zhang, T., Ren, D. et. al.,: 'Communication emitter identification based on distribution of bispectrum amplitude and phase', IET Sci. Meas. 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