Detection of Face Morphing Attacks Based on PRNU Analysis
暂无分享,去创建一个
Andreas Uhl | Christoph Busch | Christian Rathgeb | Ulrich Scherhag | Luca Debiasi | A. Uhl | C. Busch | C. Rathgeb | U. Scherhag | L. Debiasi
[1] Kiran B. Raja,et al. Detecting morphed face images , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).
[2] Nasir D. Memon,et al. Seam-carving based anonymization against image & video source attribution , 2013, 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP).
[3] Fei Peng,et al. Face Morphing Detection Using Fourier Spectrum of Sensor Pattern Noise , 2018, 2018 IEEE International Conference on Multimedia and Expo (ICME).
[4] Matti Pietikäinen,et al. A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..
[5] Davide Maltoni,et al. The magic passport , 2014, IEEE International Joint Conference on Biometrics.
[6] L. Javier García-Villalba,et al. A PRNU-based counter-forensic method to manipulate smartphone image source identification techniques , 2017, Future Gener. Comput. Syst..
[7] Adam Schmidt,et al. The put face database , 2008 .
[8] Alan J. Cooper,et al. Improved photo response non-uniformity (PRNU) based source camera identification. , 2013, Forensic science international.
[9] Gaurav Sharma,et al. Image anonymization for PRNU forensics: A set theoretic framework addressing compression resilience , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[10] Lei Zhang,et al. A Probabilistic Collaborative Representation Based Approach for Pattern Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] C. Busch,et al. Evaluation of image compression algorithms for fingerprint and face recognition systems , 2005, Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop.
[12] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[13] Miroslav Goljan,et al. Digital camera identification from sensor pattern noise , 2006, IEEE Transactions on Information Forensics and Security.
[14] Aras Asaad. Automatic Detection of Image Morphing by Topology-based Analysis , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).
[15] Jiwu Huang,et al. Enhancing Source Camera Identification Performance With a Camera Reference Phase Sensor Pattern Noise , 2012, IEEE Transactions on Information Forensics and Security.
[16] Arun Ross,et al. Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition , 2019, 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA).
[17] Rainer Böhme,et al. Can we trust digital image forensics? , 2007, ACM Multimedia.
[18] Anna Hilsmann,et al. Reflection Analysis for Face Morphing Attack Detection , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).
[19] Takeo Kanade,et al. Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.
[20] David J Robertson,et al. Detecting morphed passport photos: a training and individual differences approach , 2018, Cognitive research: principles and implications.
[21] Luuk J. Spreeuwers,et al. Towards Robust Evaluation of Face Morphing Detection , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).
[22] George Wolberg,et al. Image morphing: a survey , 1998, The Visual Computer.
[23] Davide Cozzolino,et al. Attacking the triangle test in sensor-based camera identification , 2014, 2014 IEEE International Conference on Image Processing (ICIP).
[24] Xufeng Lin,et al. Enhancing Sensor Pattern Noise via Filtering Distortion Removal , 2016, IEEE Signal Processing Letters.
[25] Mo Chen,et al. Defending Against Fingerprint-Copy Attack in Sensor-Based Camera Identification , 2011, IEEE Transactions on Information Forensics and Security.
[26] Chi Fang,et al. Histogram of the oriented gradient for face recognition , 2011 .
[27] Jana Dittmann,et al. Generalized Benford's Law for Blind Detection of Morphed Face Images , 2018, IH&MMSec.
[28] Kiran B. Raja,et al. Detecting Face Morphing Attacks with Collaborative Representation of Steerable Features , 2018, CVIP.
[29] Rainer Böhme,et al. The 'Dresden Image Database' for benchmarking digital image forensics , 2010, SAC '10.
[30] Davide Maltoni,et al. Face Demorphing , 2018, IEEE Transactions on Information Forensics and Security.
[31] Zeno Geradts,et al. Using Anisotropic Diffusion for Efficient Extraction of Sensor Noise in Camera Identification , 2012, Journal of forensic sciences.
[32] Venkata Udaya Sameer,et al. Deep Learning Based Counter-Forensic Image Classification for Camera Model Identification , 2017, IWDW.
[33] J. Fridrich,et al. Digital image forensics , 2009, IEEE Signal Processing Magazine.
[34] Mislav Grgic,et al. SCface – surveillance cameras face database , 2011, Multimedia Tools and Applications.
[35] Anna Hilsmann,et al. Detection of Face Morphing Attacks by Deep Learning , 2017, IWDW.
[36] Aksh Patel. Image Morphing Algorithm: A Survey , 2015 .
[37] Anna Hilsmann,et al. Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks , 2018, ArXiv.
[38] Sabah Jassim,et al. Topological Data Analysis for Image Tampering Detection , 2017, IWDW.
[39] Matthijs C. Dorst. Distinctive Image Features from Scale-Invariant Keypoints , 2011 .
[40] Aleix M. Martinez,et al. The AR face database , 1998 .
[41] Kannan Ramchandran,et al. Spatially adaptive statistical modeling of wavelet image coefficients and its application to denoising , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[42] Naser Damer,et al. Detecting Face Morphing Attacks by Analyzing the Directed Distances of Facial Landmarks Shifts , 2018, GCPR.
[43] Xiangui Kang,et al. Fast Source Camera Identification Using Content Adaptive Guided Image Filter , 2016, Journal of forensic sciences.
[44] Joshua Correll,et al. The Chicago face database: A free stimulus set of faces and norming data , 2015, Behavior research methods.
[45] Xufeng Lin,et al. Preprocessing Reference Sensor Pattern Noise via Spectrum Equalization , 2016, IEEE Transactions on Information Forensics and Security.
[46] Davide Maltoni,et al. Face demorphing in the presence of facial appearance variations , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).
[47] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[48] Davide Maltoni,et al. On the Effects of Image Alterations on Face Recognition Accuracy , 2016, Face Recognition Across the Imaging Spectrum.
[49] Mauro Barni,et al. Countering the Pooled Triangle Test for PRNU-based camera identification , 2018, 2018 IEEE International Workshop on Information Forensics and Security (WIFS).
[50] Husrev T. Sencar,et al. A study of the robustness of PRNU-based camera identification , 2009, Electronic Imaging.
[51] Naser Damer,et al. MorGAN: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Generative Adversarial Network , 2018, 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS).
[52] Christoph Busch,et al. Towards Detection of Morphed Face Images in Electronic Travel Documents , 2018, 2018 13th IAPR International Workshop on Document Analysis Systems (DAS).
[53] Christoph Busch,et al. PRNU-based detection of morphed face images , 2018, 2018 International Workshop on Biometrics and Forensics (IWBF).
[54] Chang-Tsun Li. Source camera identification using enhanced sensor pattern noise , 2010, IEEE Trans. Inf. Forensics Secur..
[55] Fouad Khelifi,et al. A novel image filtering approach for sensor fingerprint estimation in source camera identification , 2015, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
[56] Jana Dittmann,et al. Automatic Generation and Detection of Visually Faultless Facial Morphs , 2017, VISIGRAPP.
[57] Richa Singh,et al. SWAPPED! Digital face presentation attack detection via weighted local magnitude pattern , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).
[58] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Andrew Zisserman,et al. Deep Face Recognition , 2015, BMVC.
[60] Davis E. King,et al. Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..
[61] Sébastien Marcel,et al. Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting , 2017, 2017 International Conference of the Biometrics Special Interest Group (BIOSIG).
[62] Jian Sun,et al. Blessing of Dimensionality: High-Dimensional Feature and Its Efficient Compression for Face Verification , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[63] Jessica Fridrich,et al. Sensor Defects in Digital Image Forensic , 2013 .
[64] Ahmet Emir Dirik,et al. Forensic use of photo response non-uniformity of imaging sensors and a counter method. , 2014, Optics express.
[65] Patrick J. Flynn,et al. Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[66] C. Thomaz,et al. A new ranking method for principal components analysis and its application to face image analysis , 2010, Image Vis. Comput..
[67] Roberto Caldelli,et al. An analysis on attacker actions in fingerprint-copy attack in source camera identification , 2011, 2011 IEEE International Workshop on Information Forensics and Security.
[68] Jun Wang,et al. A 3D facial expression database for facial behavior research , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).
[69] Arun Ross,et al. Impact of photometric transformations on PRNU estimation schemes: A case study using near infrared ocular images , 2018, 2018 International Workshop on Biometrics and Forensics (IWBF).
[70] Francesco G. B. De Natale,et al. Performance comparison of denoising filters for source camera identification , 2011, Electronic Imaging.
[71] Chang-Tsun Li,et al. Color-Decoupled Photo Response Non-Uniformity for Digital Image Forensics , 2012, IEEE Transactions on Circuits and Systems for Video Technology.
[72] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[73] Kiran B. Raja,et al. Transferable Deep-CNN Features for Detecting Digital and Print-Scanned Morphed Face Images , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[74] Andreas Uhl,et al. Iris-sensor authentication using camera PRNU fingerprints , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).
[75] Christoph Busch,et al. Detecting Morphed Face Images Using Facial Landmarks , 2018, ICISP.
[76] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[77] Xiangui Kang,et al. A context-adaptive SPN predictor for trustworthy source camera identification , 2014, EURASIP J. Image Video Process..
[78] Christoph Busch,et al. Morph Deterction from Single Face Image: a Multi-Algorithm Fusion Approach , 2018, ICBEA '18.
[79] Mahadev Satyanarayanan,et al. OpenFace: A general-purpose face recognition library with mobile applications , 2016 .
[80] Kiran B. Raja,et al. On the vulnerability of face recognition systems towards morphed face attacks , 2017, 2017 5th International Workshop on Biometrics and Forensics (IWBF).
[81] Esa Rahtu,et al. BSIF: Binarized statistical image features , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).
[82] Teun Baar,et al. Improving source camera identification using a simplified total variation based noise removal algorithm , 2013, Digit. Investig..
[83] Tom Neubert,et al. Face Morphing Detection: An Approach Based on Image Degradation Analysis , 2017, IWDW.
[84] Jan Lukás,et al. Camera identification from printed images , 2008, Electronic Imaging.
[85] Jana Dittmann,et al. Benchmarking face morphing forgery detection: Application of stirtrace for impact simulation of different processing steps , 2017, 2017 5th International Workshop on Biometrics and Forensics (IWBF).
[86] Luc Van Gool,et al. Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..
[87] Harry Wechsler,et al. The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..
[88] Kiran B. Raja,et al. Face morphing versus face averaging: Vulnerability and detection , 2017, 2017 IEEE International Joint Conference on Biometrics (IJCB).
[89] Matthias Kirchner,et al. Unexpected artefacts in PRNU-based camera identification: a 'Dresden Image Database' case-study , 2012, MM&Sec '12.
[90] Christoph Busch,et al. Face Recognition Systems Under Morphing Attacks: A Survey , 2019, IEEE Access.
[91] Sushma Venkatesh,et al. Towards making Morphing Attack Detection robust using hybrid Scale-Space Colour Texture Features , 2019, 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA).
[92] Jana Dittmann,et al. Modeling Attacks on Photo-ID Documents and Applying Media Forensics for the Detection of Facial Morphing , 2017, IH&MMSec.
[93] Christoph Busch,et al. Performance variation of morphed face image detection algorithms across different datasets , 2018, 2018 International Workshop on Biometrics and Forensics (IWBF).
[94] Raul Vicente-Garcia,et al. Morphing Detection Using a General- Purpose Face Recognition System , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).
[95] Christoph Busch,et al. PRNU Variance Analysis for Morphed Face Image Detection , 2018, 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS).