{"list":"other","total":118,"rows":[{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1610.02391","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization","created":"2016-10-07","cited_by_count":6733},{"id":"1705.07874","title":"A Unified Approach to Interpreting Model Predictions","created":"2017-05-22","cited_by_count":6555},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1512.04150","title":"Learning Deep Features for Discriminative Localization","created":"2015-12-14","cited_by_count":2436},{"id":"1312.6034","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","created":"2013-12-20","cited_by_count":2371},{"id":"1311.2901","title":"Visualizing and Understanding Convolutional Networks","created":"2013-11-12","cited_by_count":2290},{"id":"1704.02685","title":"Learning Important Features Through Propagating Activation Differences","created":"2017-04-10","cited_by_count":1458},{"id":"1901.05555","title":"Class-Balanced Loss Based on Effective Number of Samples","created":"2019-01-16","cited_by_count":1245},{"id":"1706.03825","title":"SmoothGrad: removing noise by adding noise","created":"2017-06-12","cited_by_count":1109},{"id":"1906.07413","title":"Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss","created":"2019-06-18","cited_by_count":989},{"id":"1801.09414","title":"CosFace: Large Margin Cosine Loss for Deep Face Recognition","created":"2018-01-29","cited_by_count":971},{"id":"1611.01236","title":"Adversarial Machine Learning at Scale","created":"2016-11-04","cited_by_count":936},{"id":"1805.12152","title":"Robustness May Be at Odds with Accuracy","created":"2018-05-30","cited_by_count":880},{"id":"1607.08221","title":"MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition","created":"2016-07-27","cited_by_count":624},{"id":"1704.03296","title":"Interpretable Explanations of Black Boxes by Meaningful Perturbation","created":"2017-04-11","cited_by_count":612},{"id":"1711.00117","title":"Countering Adversarial Images using Input Transformations","created":"2017-10-31","cited_by_count":520},{"id":"1412.0035","title":"Understanding Deep Image Representations by Inverting Them","created":"2014-11-26","cited_by_count":403},{"id":"1711.01991","title":"Mitigating Adversarial Effects Through Randomization","created":"2017-11-06","cited_by_count":383},{"id":"1805.06605","title":"Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models","created":"2018-05-17","cited_by_count":362},{"id":"1506.06579","title":"Understanding Neural Networks Through Deep Visualization","created":"2015-06-22","cited_by_count":334},{"id":"1812.03411","title":"Feature Denoising for Improving Adversarial Robustness","created":"2018-12-09","cited_by_count":314},{"id":"1705.09064","title":"MagNet: a Two-Pronged Defense against Adversarial Examples","created":"2017-05-25","cited_by_count":269},{"id":"1703.00410","title":"Detecting Adversarial Samples from Artifacts","created":"2017-03-01","cited_by_count":245},{"id":"1710.10766","title":"PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples","created":"2017-10-30","cited_by_count":244},{"id":"1702.04267","title":"On Detecting Adversarial Perturbations","created":"2017-02-14","cited_by_count":232},{"id":"1803.06373","title":"Adversarial Logit Pairing","created":"2018-03-16","cited_by_count":224},{"id":"1702.06280","title":"On the (Statistical) Detection of Adversarial Examples","created":"2017-02-21","cited_by_count":180},{"id":"2006.04924","title":"A Self-supervised Approach for Adversarial Robustness","created":"2020-06-08","cited_by_count":180},{"id":"1904.04433","title":"Efficient Decision-based Black-box Adversarial Attacks on Face Recognition","created":"2019-04-09","cited_by_count":175},{"id":"1904.09658","title":"Probabilistic Face Embeddings","created":"2019-04-21","cited_by_count":173},{"id":"1803.01442","title":"Stochastic Activation Pruning for Robust Adversarial Defense","created":"2018-03-05","cited_by_count":171},{"id":"1506.02753","title":"Inverting Visual Representations with Convolutional Networks","created":"2015-06-09","cited_by_count":170},{"id":"1803.11097","title":"Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision","created":"2018-03-29","cited_by_count":167},{"id":"1911.10692","title":"Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning","created":"2019-11-25","cited_by_count":145},{"id":"2004.14043","title":"Single-Side Domain Generalization for Face Anti-Spoofing","created":"2020-04-29","cited_by_count":139},{"id":"1808.01688","title":"Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models","created":"2018-08-05","cited_by_count":135},{"id":"1803.05787","title":"Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples","created":"2018-03-14","cited_by_count":99},{"id":"1906.07927","title":"SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing","created":"2019-06-19","cited_by_count":93},{"id":"2003.02488","title":"Demographic Bias in Biometrics: A Survey on an Emerging Challenge","created":"2020-03-05","cited_by_count":93},{"id":"2103.01592","title":"A Comprehensive Study on Face Recognition Biases Beyond Demographics","created":"2021-03-02","cited_by_count":86},{"id":"1612.07767","title":"Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics","created":"2016-12-22","cited_by_count":77},{"id":"1907.04047","title":"Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection","created":"2019-07-09","cited_by_count":76},{"id":"1903.09730","title":"Generative Adversarial Minority Oversampling","created":"2019-03-22","cited_by_count":73},{"id":"1904.02860","title":"Deep Tree Learning for Zero-shot Face Anti-Spoofing","created":"2019-04-05","cited_by_count":73},{"id":"1703.00832","title":"On the Reconstruction of Face Images from Deep Face Templates","created":"2017-03-02","cited_by_count":67},{"id":"2006.07576","title":"Mitigating Face Recognition Bias via Group Adaptive Classifier","created":"2020-06-13","cited_by_count":67},{"id":"2007.09273","title":"On Disentangling Spoof Trace for Generic Face Anti-Spoofing","created":"2020-07-17","cited_by_count":62},{"id":"1908.05008","title":"AdvFaces: Adversarial Face Synthesis","created":"2019-08-14","cited_by_count":60},{"id":"1812.10659","title":"Low Latency Privacy Preserving Inference","created":"2018-12-27","cited_by_count":57},{"id":"1707.06728","title":"Efficient Defenses Against Adversarial Attacks","created":"2017-07-21","cited_by_count":53},{"id":"1704.04960","title":"Adversarial and Clean Data Are Not Twins","created":"2017-04-17","cited_by_count":52},{"id":"1909.09901","title":"Learning a Fixed-Length Fingerprint Representation","created":"2019-09-21","cited_by_count":49},{"id":"2011.08019","title":"On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing","created":"2020-11-16","cited_by_count":44},{"id":"2004.00445","title":"Learning to Cluster Faces via Confidence and Connectivity Estimation","created":"2020-04-01","cited_by_count":42},{"id":"1608.00530","title":"Early Methods for Detecting Adversarial Images","created":"2016-08-01","cited_by_count":36},{"id":"1709.02228","title":"FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction","created":"2017-09-07","cited_by_count":36},{"id":"2007.11457","title":"Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks","created":"2020-07-22","cited_by_count":36},{"id":"2101.05084","title":"This Face Does Not Exist ... But It Might Be Yours! Identity Leakage in Generative Models","created":"2020-12-10","cited_by_count":33},{"id":"2104.02156","title":"Unified Detection of Digital and Physical Face Attacks","created":"2021-04-05","cited_by_count":31},{"id":"1805.00611","title":"Towards Interpretable Face Recognition","created":"2018-05-02","cited_by_count":30},{"id":"1805.00577","title":"Secure Face Matching Using Fully Homomorphic Encryption","created":"2018-05-01","cited_by_count":29},{"id":"2007.11469","title":"Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks","created":"2020-07-22","cited_by_count":29},{"id":"1712.04489","title":"Fingerprint Spoof Buster","created":"2017-12-12","cited_by_count":27},{"id":"2009.00749","title":"Iris Liveness Detection Competition (LivDet-Iris) -- The 2020 Edition","created":"2020-09-01","cited_by_count":27},{"id":"1604.00989","title":"Clustering Millions of Faces by Identity","created":"2016-04-04","cited_by_count":26},{"id":"2003.12197","title":"HERS: Homomorphically Encrypted Representation Search","created":"2020-03-27","cited_by_count":26},{"id":"2007.01381","title":"D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector","created":"2020-07-02","cited_by_count":26},{"id":"2104.03008","title":"FedFace: Collaborative Learning of Face Recognition Model","created":"2021-04-07","cited_by_count":26},{"id":"1404.3840","title":"Surpassing Human-Level Face Verification Performance on LFW with GaussianFace","created":"2014-04-15","cited_by_count":24},{"id":"1804.00194","title":"Presentation Attack Detection for Iris Recognition: An Assessment of the State of the Art","created":"2018-03-31","cited_by_count":24},{"id":"1901.00536","title":"Visualizing Deep Similarity Networks","created":"2019-01-02","cited_by_count":24},{"id":"1809.08999","title":"Fast Geometrically-Perturbed Adversarial Faces","created":"2018-09-24","cited_by_count":22},{"id":"1410.1980","title":"Deep Representations for Iris, Face, and Fingerprint Spoofing Detection","created":"2014-10-08","cited_by_count":21},{"id":"1711.03990","title":"Longitudinal Study of Child Face Recognition","created":"2017-11-10","cited_by_count":18},{"id":"2006.02834","title":"Look Locally Infer Globally: A Generalizable Face Anti-Spoofing Approach","created":"2020-06-04","cited_by_count":18},{"id":"1905.00639","title":"LivDet in Action - Fingerprint Liveness Detection Competition 2019","created":"2019-05-02","cited_by_count":17},{"id":"2002.02942","title":"On the Robustness of Face Recognition Algorithms Against Attacks and Bias","created":"2020-02-07","cited_by_count":16},{"id":"2012.02374","title":"CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection","created":"2020-12-04","cited_by_count":16},{"id":"1912.02918","title":"Detection of Face Recognition Adversarial Attacks","created":"2019-12-05","cited_by_count":15},{"id":"2011.14218","title":"FaceGuard: A Self-Supervised Defense Against Adversarial Face Images","created":"2020-11-28","cited_by_count":15},{"id":"1704.02203","title":"Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption","created":"2017-04-07","cited_by_count":14},{"id":"1812.10213","title":"End-to-End Latent Fingerprint Search","created":"2018-12-26","cited_by_count":14},{"id":"2006.13252","title":"Iris Presentation Attack Detection: Where Are We Now?","created":"2020-06-23","cited_by_count":14},{"id":"1708.02582","title":"Cascade Adversarial Machine Learning Regularized with a Unified Embedding","created":"2017-08-08","cited_by_count":13},{"id":"1901.03918","title":"Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier","created":"2019-01-13","cited_by_count":13},{"id":"1905.04717","title":"Some Research Problems in Biometrics: The Future Beckons","created":"2019-05-12","cited_by_count":13},{"id":"1910.05657","title":"How are attributes expressed in face DCNNs?","created":"2019-10-12","cited_by_count":13},{"id":"2003.03151","title":"Demographic Bias in Presentation Attack Detection of Iris Recognition Systems","created":"2020-03-06","cited_by_count":13},{"id":"1902.11065","title":"Biometric Presentation Attack Detection: Beyond the Visible Spectrum","created":"2019-02-28","cited_by_count":12},{"id":"1912.07195","title":"Fingerprint Synthesis: Search with 100 Million Prints","created":"2019-12-16","cited_by_count":11},{"id":"1812.11574","title":"Fingerprint Presentation Attack Detection: Generalization and Efficiency","created":"2018-12-30","cited_by_count":9},{"id":"2104.02239","title":"IronMask: Modular Architecture for Protecting Deep Face Template","created":"2021-04-06","cited_by_count":9},{"id":"1912.03737","title":"Universal Material Translator: Towards Spoof Fingerprint Generalization","created":"2019-12-08","cited_by_count":8},{"id":"1712.09392","title":"RaspiReader: Open Source Fingerprint Reader","created":"2017-12-26","cited_by_count":7},{"id":"2004.02941","title":"Fingerprint Presentation Attack Detection: A Sensor and Material Agnostic Approach","created":"2020-04-06","cited_by_count":7},{"id":"2008.08220","title":"Open Source Iris Recognition Hardware and Software with Presentation Attack Detection","created":"2020-08-19","cited_by_count":7},{"id":"2010.03624","title":"Infant-ID: Fingerprints for Global Good","created":"2020-10-07","cited_by_count":7},{"id":"1901.05633","title":"Deep Transfer Across Domains for Face Anti-spoofing","created":"2019-01-17","cited_by_count":6}],"next_cursor":"100"}