{"list":"other","total":69,"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":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"id":"1503.03832","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","created":"2015-03-12","cited_by_count":2849},{"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":"1712.05526","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","created":"2017-12-15","cited_by_count":1055},{"id":"1911.09070","title":"EfficientDet: Scalable and Efficient Object Detection","created":"2019-11-20","cited_by_count":1020},{"id":"1604.07316","title":"End to End Learning for Self-Driving Cars","created":"2016-04-25","cited_by_count":1009},{"id":"1412.6806","title":"Striving for Simplicity: The All Convolutional Net","created":"2014-12-21","cited_by_count":990},{"id":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"1704.05796","title":"Network Dissection: Quantifying Interpretability of Deep Visual Representations","created":"2017-04-19","cited_by_count":708},{"id":"1801.04062","title":"MINE: Mutual Information Neural Estimation","created":"2018-01-12","cited_by_count":683},{"id":"1802.10026","title":"Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs","created":"2018-02-27","cited_by_count":588},{"id":"1805.12185","title":"Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks","created":"2018-05-30","cited_by_count":580},{"id":"1811.00636","title":"Spectral Signatures in Backdoor Attacks","created":"2018-11-01","cited_by_count":484},{"id":"1902.06531","title":"STRIP: A Defence Against Trojan Attacks on Deep Neural Networks","created":"2019-02-18","cited_by_count":459},{"id":"1811.03728","title":"Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering","created":"2018-11-09","cited_by_count":456},{"id":"2007.08745","title":"Backdoor Learning: A Survey","created":"2020-07-17","cited_by_count":426},{"id":"1910.00033","title":"Hidden Trigger Backdoor Attacks","created":"2019-09-30","cited_by_count":371},{"id":"2007.02343","title":"Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks","created":"2020-07-05","cited_by_count":349},{"id":"2101.05930","title":"Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks","created":"2021-01-15","cited_by_count":324},{"id":"1912.02771","title":"Label-Consistent Backdoor Attacks","created":"2019-12-05","cited_by_count":296},{"id":"1904.08653","title":"Fooling automated surveillance cameras: adversarial patches to attack person detection","created":"2019-04-18","cited_by_count":268},{"id":"1909.08072","title":"Adversarial Attacks and Defenses in Images, Graphs and Text: A Review","created":"2019-09-17","cited_by_count":237},{"id":"1910.03137","title":"Detecting AI Trojans Using Meta Neural Analysis","created":"2019-10-08","cited_by_count":199},{"id":"2005.03823","title":"Blind Backdoors in Deep Learning Models","created":"2020-05-08","cited_by_count":182},{"id":"1710.00942","title":"Neural Trojans","created":"2017-10-03","cited_by_count":153},{"id":"1812.00292","title":"SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems","created":"2018-12-02","cited_by_count":152},{"id":"2003.03675","title":"Dynamic Backdoor Attacks Against Machine Learning Models","created":"2020-03-07","cited_by_count":151},{"id":"1906.10842","title":"Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs","created":"2019-06-26","cited_by_count":146},{"id":"2006.14580","title":"Backdoor Attacks Against Deep Learning Systems in the Physical World","created":"2020-06-25","cited_by_count":134},{"id":"2005.00060","title":"Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness","created":"2020-04-30","cited_by_count":132},{"id":"2006.11165","title":"Backdoor Attacks to Graph Neural Networks","created":"2020-06-19","cited_by_count":128},{"id":"1908.01763","title":"TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems","created":"2019-08-02","cited_by_count":122},{"id":"1906.02825","title":"XRAI: Better Attributions Through Regions","created":"2019-06-06","cited_by_count":116},{"id":"2006.12557","title":"Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks","created":"2020-06-22","cited_by_count":116},{"id":"1909.05193","title":"TBT: Targeted Neural Network Attack with Bit Trojan","created":"2019-09-10","cited_by_count":113},{"id":"2102.05123","title":"Backdoor Scanning for Deep Neural Networks through K-Arm Optimization","created":"2021-02-09","cited_by_count":98},{"id":"2006.08131","title":"An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks","created":"2020-06-15","cited_by_count":97},{"id":"1804.00097","title":"Adversarial Attacks and Defences Competition","created":"2018-03-31","cited_by_count":96},{"id":"1911.07116","title":"Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy","created":"2019-11-16","cited_by_count":95},{"id":"2007.15802","title":"Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases","created":"2020-07-31","cited_by_count":95},{"id":"2011.09527","title":"Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff","created":"2020-11-18","cited_by_count":89},{"id":"1801.08092","title":"Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations","created":"2018-01-24","cited_by_count":88},{"id":"1910.04749","title":"Defending Neural Backdoors via Generative Distribution Modeling","created":"2019-10-10","cited_by_count":84},{"id":"2004.04692","title":"Rethinking the Trigger of Backdoor Attack","created":"2020-04-09","cited_by_count":77},{"id":"2012.07006","title":"DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation","created":"2020-12-13","cited_by_count":77},{"id":"1812.03128","title":"Backdooring Convolutional Neural Networks via Targeted Weight Perturbations","created":"2018-12-07","cited_by_count":69},{"id":"1908.02203","title":"Model Agnostic Defence against Backdoor Attacks in Machine Learning","created":"2019-08-06","cited_by_count":52},{"id":"1911.07399","title":"NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations","created":"2019-11-18","cited_by_count":50},{"id":"1904.00605","title":"Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks","created":"2019-04-01","cited_by_count":49},{"id":"2001.03274","title":"Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models","created":"2020-01-10","cited_by_count":44},{"id":"2003.07233","title":"The TrojAI Software Framework: An OpenSource tool for Embedding Trojans into Deep Learning Models","created":"2020-03-13","cited_by_count":42},{"id":"2008.07711","title":"One-pixel Signature: Characterizing CNN Models for Backdoor Detection","created":"2020-08-18","cited_by_count":34},{"id":"2007.00711","title":"ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks","created":"2020-07-01","cited_by_count":28},{"id":"1908.03369","title":"Februus: Input Purification Defense Against Trojan Attacks on Deep Neural Network Systems","created":"2019-08-09","cited_by_count":27},{"id":"2004.11368","title":"Neural Network Laundering: Removing Black-Box Backdoor Watermarks from Deep Neural Networks","created":"2020-04-22","cited_by_count":27},{"id":"2009.02326","title":"CLEANN: Accelerated Trojan Shield for Embedded Neural Networks","created":"2020-09-04","cited_by_count":22},{"id":"1902.09972","title":"Design of intentional backdoors in sequential models","created":"2019-02-26","cited_by_count":21},{"id":"1909.02742","title":"Invisible Backdoor Attacks on Deep Neural Networks via Steganography and Regularization","created":"2019-09-06","cited_by_count":18},{"id":"2007.14433","title":"Cassandra: Detecting Trojaned Networks from Adversarial Perturbations","created":"2020-07-28","cited_by_count":15},{"id":"2103.10274","title":"TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation","created":"2021-03-18","cited_by_count":13},{"id":"1912.00271","title":"Biometrics Recognition Using Deep Learning: A Survey","created":"2019-11-30","cited_by_count":11},{"id":"2002.08313","title":"NNoculation: Catching BadNets in the Wild","created":"2020-02-19","cited_by_count":9},{"id":"2102.01815","title":"TAD: Trigger Approximation based Black-box Trojan Detection for AI","created":"2021-02-03","cited_by_count":9},{"id":"2007.08142","title":"Odyssey: Creation, Analysis and Detection of Trojan Models","created":"2020-07-16","cited_by_count":7}],"next_cursor":null}