Lee, Hung-Yi

Metaxas, Dimitris N.

Schuller, Björn W.

Zhang, Zixing

English

OPUS Augsburg

414                                                                                  Guest EditorialSpecial Issue on Adversarial Learning inComputational IntelligenceI. INTRODUCTIONADVERSARIAL learning has attracted tremendous atten-tion in the community of machine learning over the pastfew years. It normally integrates two components that contestwith each other in a two-player zero-sum game. Since its birthin 2014, adversarial learning has been widely applied to notonly the generation of realistic images, but also many otherresearch topics, such as data augmentation, domain adaptation,and adversarial attack, often leading to appealing performance.However, we have just witnessed the early rise of this technique,and still confront many challenges, for example, the mode col-lapse problem, and the interpretability of its results and failures.Computational Intelligence (CI) technologies are expected toprovide efficient solutions to deal with the raised challenges.Moreover, most of the previous adversarial-learning studies arelargely limited in addressing static images or feature vectors. Itstill remains largely an open question of how adversarial learningperforms for other complex and temporally variational signalsor modalities, such as speech and text.This special issue aims to capture the most recent advances ofadversarial learning from both theoretical and empirical perspec-tives. Moreover, it attempts to present its novel applications toother domains beyond image generation. Following a rigorouspeer-review process, seven papers out of 20 received submis-sions have been accepted for inclusion in this special issue.The topics of these papers range from computer vision, naturallanguage processing, and speech processing to cyber networks.Specifically, the special issue is organized as follows.In the first paper, “Improving Adversarial Neural MachineTranslation for Morphologically Rich Language”, Mi, Xie andZhang utilize generative adversarial networks (GANs) to im-prove the quality of translated language in neural machinetranslation (NMT). For NMT, when training the discriminatorof a GAN, the conventional methods concatenate the word em-beddings of two languages as input, while considering only onereference for the translated text. These procedures, however, arenot reasonable in morphologically rich languages. To this end,the authors extend these methods by exploiting morphologicalword embedding as inputs of the discriminator of GAN, andusing multiple reference translations instead of a single one.This method significantly improves the NMT performance oneight NMT tasks.                                                The next paper “Hardening Random Forest Cyber DetectorsAgainst Adversarial Attacks” by Apruzzese, Andreolini, Cola-janni and Marchetti, improves the model training strategy toenhance the model robustness in the context of a cyber attack.Conventionally, the machine learning-based cyber detectors arevulnerable to the targeted adversarial attacks, which partiallyowns to the rigid classification produced by hard class labels oftraining samples. To solve this issue, the authors introduce somedegree of flexibility and uncertainty in the training process byusing probability labels, which allows the algorithm to captureadditional information between classes such as similarity andreduces the weakness of adversarial attack.In the third paper “Static2Dynamic: Video Inference froma Deep Glimpse”, Yeh, Liu, Chiu and Wang utilize GANs toimprove the quality of synthesized videos in video inference. Thevideo inference aims to infer a sequence from non-consecutiveframes (images). For this purpose, the authors design a novelGAN structure, namely Stochastic and Recurrent ConditionalGAN. Especially, the generator component contains an imageencoder and a recurrent neural network (RNN) based temporalencoder to obtain the prior distribution of the given images. Theoutputs the encoders are then fed into another RNN-based tem-poral decoder and an image decoder to recover the original videosequence. The capability of this model is also shown in solvingother video generation tasks, such as video interpolations andvideo predictions.The fourth paper entitled “A System-Driven Taxonomy ofAttacks and Defenses in Adversarial Machine Learning” bySadeghi, Banerjee and Gupta, provides a comprehensive adver-sarial attack and defense survey, to help the researchers fromthe CI community select and design more robust machine learn-ing model. This survey contributes to building a fine-grainedsystem-driven taxonomy to specify adversarial system modelsin an unambiguous manner.The paper “Unsupervised Representation Disentanglementusing Cross Domain Features and Adversarial Learning in Vari-ational Autoencoder based Voice Conversion”, Huang, Luo,Hwang, Lo, Peng, Tsao and Wang exploit both the advantagesof GAN and Domain adversarial training (DAT) for voiceconversion (VC), where one of the major issues is how todisentangle the speech content and speaker-related information.In this paper, the authors present an extended cross-domainvariational autoencoder VC framework, in which a GAN is usedto approximate the distribution of real speech signals better,and DAT is applied to the latent code as an explicit constraint                                                                                                                                                                                                                                                          415to eliminate speaker-dependent factors and retain the speechcontent.DAT has shown promising performance in mitigating thedomain mismatch, whereas it cannot guarantee that the learnedrepresentation spaces from different domains are class-wisealigned. In the paper “Learning Class-aligned and GeneralizedDomain-invariant Representations for Speech Emotion Recog-nition”, Xiao, Zhao and Li propose a class-aligned DAT, byadding a limited amount of annotated samples from differentdomains for supervised classification. This algorithm showsto be effective by an empirical evaluation in speech emotionrecognition.Last but not least, Chaturvedi and Garain in their paper“Attacking VQA Systems via Adversarial Background Noise”investigate adversarial attacks for visual question answeringsystems by only modifying the background pixels. It visualizeshow an attention mechanism can be distracted by noise, and whatis the difference of the disturbing noise for distinct models.II. CONCLUSIONFrom this review, one can see that the special issue generallyachieved its goal of capturing a snapshot of cutting-edge al-gorithms and applications of adversarial learning in a variety ofdomains. Particularly, the first and third articles focus on design-ing and implementing novel GAN structures and correspondingtraining strategies; the six article attempts to improve the classicDAT algorithms by taking limited supervised information intoaccount; the fourth article coordinates GAN with DAT to utmostexplore the advantage of adversarial learning; and the second,fourth, and seventh articles investigate or review the advancedadversarial attacks and defense strategies. Applications are wellrepresented in contributions to this special issue, comprising theapplications for NMT, cyber security, video inference, speechemotion recognition, voice conversion, and VQA. The data inmost of these applications are in sequence, which is complexand dynamic than static images. Because of these high-qualitycontributions and their interesting findings, we foresee that thisspecial issue will facilitate the future research and developmentwork of adversarial learning in CI.ACKNOWLEDGMENTThe guest editors are thankful to all the authors who submittedtheir excellent contributions to this special issue, and appreciateall the reviewers for dedicating their efforts in completing timelyand constructive reviews. The guest editors especially thankthe Editor-in-Chief, Prof Yew-Soon Ong, and members of theeditorial team for their support during the editing process of thisSpecial Issue.ZIXING ZHANG, Guest EditorDepartment of ComputingImperial College LondonLondon SW7 2AZ, UKzixing.zhang@tum.deDIMITRIS N. METAXAS, Guest EditorDepartment of Computer ScienceRutgers The State University of New JerseyNJ 08854-8019, USAdnm@cs.rutgers.eduHUNG-YI LEE, Guest EditorDepartment of Electrical EngineeringNational Taiwan Universityhungyilee@ntu.edu.twBJÖRN W. SCHULLER, Guest EditorChair of Embedded Intelligence for HealthCare and WellbeingUniversity of AugsburgAugsburg 86159, Germanyschuller@ieee.orgZixing Zhang received his master degree in physical electronics from the Beijing Universityof Posts and Telecommunications (BUPT), China, in 2010, and his PhD degree in computerengineering from the Technical University of Munich (TUM), Germany, in 2015. Currently, heis a Research Associate with the Department of Computing at Imperial College London (ICL),UK, since 2017. Before that, he was a Postdoctoral Researcher at the University of Passau,Germany, from 2015 to 2017. He has authored more than ninety publications in peer-reviewedbooks, journals, and conference proceedings to date. His research mainly focuses on deep learningtechnologies for speaker-centered state and health computing. He has organized special sessions,such as at the IEEE 7th Affective Computing and Intelligent Interaction (ACII) conference in2017 and at the 43nd IEEE International Conference on Acoustics, Speech, and Signal Processing(ICASSP) in 2018. Moreover, he serves as a reviewer for numerous leading-in-their fields journalsand conferences, and as a Program Committee Member and an Area Chair for many internationalconferences.416                                                                                  Dimitris N. Metaxas is a Distinguished Professor in the Computer Science Department at RutgersUniversity. He is director of the Center for Computational Biomedicine, Imaging and Modeling(CBIM) and the NSF I/UCR CARTA Center. He has also been a Tenured Faculty Member in theComputer and Information Science Department of the University of Pennsylvania. Prof. Metaxasreceived a Diploma with highest honors in Electrical Engineering and Computer Science fromthe National Technical University of Athens Greece, an M.Sc. in Computer Science from theUniversity of Maryland, College Park, and a Ph.D. in Computer Science from the Universityof Toronto. His research emphasizes the development of novel Machine Learning methods,GANs, machine learning methods for representation and understanding of scenes, shape andmotion, statistical object modeling and tracking, machine learning and sparse learning methodsfor segmentation and medical image reconstruction, deformable models and fluid simulations.Dr. Metaxas has published over 500 research articles in these areas and has graduated 57 PhDstudents. Dr. Metaxas has received several best paper awards, and holds 8 patents. He was awardeda Fulbright Fellowship in 1986, is a recipient of an NSF Research Initiation and Career awards, an ONR YIP, a Fellow of theMICCAI Society, a Fellow the American Institute of Medical and Biological Engineers and a Fellow of IEEE. He has beeninvolved with the organization of several major conferences in vision and medical image analysis, including ICCV 2007, ICCV2011, MICCAI 2008 and CVPR 2014.Hung-yi Lee received the M.S. and Ph.D. degrees from National Taiwan University (NTU),Taipei, Taiwan, in 2010 and 2012, respectively. From September 2012 to August 2013, he wasa Postdoctoral Fellow in the Research Center for Information Technology Innovation, AcademiaSinica. From September 2013 to July 2014, he was a Visiting Scientist at the Spoken LanguageSystems Group of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).He is currently an Associate Professor of the Department of Electrical Engineering of NationalTaiwan University, with a joint appointment at the Department of Computer Science & InformationEngineering of the university. His research focuses on machine learning (especially deep learning),spoken language understanding and speech recognition. He owns a YouTube channel teachingdeep learning (in Mandarin) with more than 4M views and 55k subscribers. He gave the tutorialon generative adversarial network (GAN) at ICASSP 2018, APSIPA 2018 and ISCSLP 2018.Björn W. Schuller received his diploma in 1999, his doctoral degree in 2006, and his habilitationand Adjunct Teaching Professorship in the subject area of Signal Processing and MachineIntelligence in 2012, all in electrical engineering and information technology from TUM inMunich/Germany. He is Professor of Artificial Intelligence in the Department of Computingat Imperial College London/UK, where he heads GLAM, Full Professor Chair of EmbeddedIntelligence for Health Care and Wellbeing at the University of Augsburg/Germany, and CEO ofaudEERING. He was previously Full Professor Chair of Complex and Intelligent Systems at theUniversity of Passau/Germany. Professor Schuller is President-emeritus of the Association forthe Advancement of Affective Computing (AAAC), Fellow of the IEEE, and Senior Member ofthe ACM. He (co-)authored five books and more than 900 publications in peer-reviewed books,journals, and conference proceedings leading to more than overall 29 000 citations (h-index =79). Schuller is former Editor in Chief of the IEEE TRANSACTIONS ON AFFECTIVE COMPUTINGnext to a multitude of further Associate and Guest Editor roles and functions as General, Program,and Area Chair.

Guest editorial: special issue on adversarial learning in computational intelligence

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Guest editorial: special issue on adversarial learning in computational intelligence

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