Reduced Residual Nets (Red-Nets): Low Powered Adversarial Outlier Detectors

The evolution of information science has seen an immense growth in multimedia data, specially in the case of CCTV live stream capture. The tremendously large volumes of multimedia data give rise to a particularly challenging problem called the outlier events of interest detection. In the wake of growing school shootings in the United States, there needs to be a rethinking of our security strategies regarding the safety of children at school utilizing multimedia data mining research. This paper proposes a novel method to identify faces of interest using live stream CCTV data. By integrating the adversarial information, the proposed framework can help imbalance facial recognition and enhance rare class mining even with trivial scores from the minority class. Experimental results on the Faces in the Wile (FIW) dataset demonstrate the effectiveness of the proposed framework with promising performance. The proposed method was implemented on a low powered Nvidia TX2 for real-time face recognition. The proposed framework was benchmarked against several existing state-of-the-art methods for accuracy, computational complexity, and real-time power measurement. The proposed method performs very well under the power and complexity constraints.

[1]  Shu-Ching Chen,et al.  Video semantic concept detection via associative classification , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[2]  Xiaoou Tang,et al.  Surpassing Human-Level Face Verification Performance on LFW with GaussianFace , 2014, AAAI.

[3]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[4]  Dan Boneh,et al.  Ensemble Adversarial Training: Attacks and Defenses , 2017, ICLR.

[5]  Bhavani M. Thuraisingham,et al.  Face Recognition Using Multiple Classifiers , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).

[6]  Chengcui Zhang,et al.  A Dynamic User Concept Pattern Learning Framework for Content-Based Image Retrieval , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[7]  Harini Kannan,et al.  Adversarial Logit Pairing , 2018, NIPS 2018.

[8]  Jianping Fan,et al.  Concept-oriented indexing of video databases: toward semantic sensitive retrieval and browsing , 2004, IEEE Transactions on Image Processing.

[9]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[10]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[11]  Xiaogang Wang,et al.  DeepID3: Face Recognition with Very Deep Neural Networks , 2015, ArXiv.

[12]  Mei-Ling Shyu,et al.  Handling nominal features in anomaly intrusion detection problems , 2005, 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05).

[13]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Min Chen,et al.  Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters , 2017, Int. J. Multim. Data Eng. Manag..

[15]  Choochart Haruechaiyasak,et al.  Mining user access behavior on the WWW , 2001, 2001 IEEE International Conference on Systems, Man and Cybernetics. e-Systems and e-Man for Cybernetics in Cyberspace (Cat.No.01CH37236).

[16]  Mei-Ling Shyu,et al.  Utilizing Context Information to Enhance Content-Based Image Classification , 2011, Int. J. Multim. Data Eng. Manag..

[17]  Rangasami L. Kashyap,et al.  Semantic Models for Multimedia Database Searching and Browsing , 2000, Advances in Database Systems.

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[20]  Stuart Harvey Rubin,et al.  A Human-Centered Multiple Instance Learning Framework for Semantic Video Retrieval , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[21]  Jung-Woo Ha,et al.  StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Shu-Ching Chen,et al.  AAFA: Associative Affinity Factor Analysis for Bot Detection and Stance Classification in Twitter , 2017, 2017 IEEE International Conference on Information Reuse and Integration (IRI).

[23]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Min Chen,et al.  Spatio-Temporal Analysis for Human Action Detection and Recognition in Uncontrolled Environments , 2015, Int. J. Multim. Data Eng. Manag..

[25]  Dale Schuurmans,et al.  Learning with a Strong Adversary , 2015, ArXiv.

[26]  Min Chen,et al.  A latent semantic indexing based method for solving multiple instance learning problem in region-based image retrieval , 2005, Seventh IEEE International Symposium on Multimedia (ISM'05).

[27]  Mei-Ling Shyu,et al.  Mining Anomalies in Medicare Big Data Using Patient Rule Induction Method , 2017, 2017 IEEE Third International Conference on Multimedia Big Data (BigMM).

[28]  Shu-Ching Chen,et al.  Feature Selection Using Correlation and Reliability Based Scoring Metric for Video Semantic Detection , 2010, 2010 IEEE Fourth International Conference on Semantic Computing.

[29]  Min Chen,et al.  Image database retrieval utilizing affinity relationships , 2003, MMDB '03.

[30]  Aleksander Madry,et al.  Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.

[31]  Pascal Frossard,et al.  Analysis of classifiers’ robustness to adversarial perturbations , 2015, Machine Learning.

[32]  Shu-Ching Chen,et al.  Network intrusion detection through Adaptive Sub-Eigenspace Modeling in multiagent systems , 2007, ACM Trans. Auton. Adapt. Syst..

[33]  Megha Wankhade,et al.  FACE RECOGNITION USING DISCRETE WAVELET TRANSFORMS , 2012 .

[34]  Xin Huang,et al.  User Concept Pattern Discovery Using Relevance Feedback And Multiple Instance Learning For Content-Based Image Retrieval , 2002, MDM/KDD.

[35]  Mei-Ling Shyu,et al.  Sparse Linear Integration of Content and Context Modalities for Semantic Concept Retrieval , 2015, IEEE Transactions on Emerging Topics in Computing.

[36]  Colin Raffel,et al.  Is Generator Conditioning Causally Related to GAN Performance? , 2018, ICML.

[37]  Xiuqi Li,et al.  An effective content-based visual image retrieval system , 2002, Proceedings 26th Annual International Computer Software and Applications.

[38]  Min Chen,et al.  Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework , 2008, IEEE Transactions on Multimedia.

[39]  Shu-Ching Chen,et al.  Enhancing Multimedia Imbalanced Concept Detection Using VIMP in Random Forests , 2016, 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI).

[40]  Jian Sun,et al.  A Practical Transfer Learning Algorithm for Face Verification , 2013, 2013 IEEE International Conference on Computer Vision.

[41]  Mei-Ling Shyu,et al.  Effective Feature Space Reduction with Imbalanced Data for Semantic Concept Detection , 2008, 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008).

[42]  Seyed-Mohsen Moosavi-Dezfooli,et al.  DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Mahadev Satyanarayanan,et al.  OpenFace: A general-purpose face recognition library with mobile applications , 2016 .