Battista Biggio

Computer, March, 2024

Rethinking data augmentation for adversarial robustness.

[BibT_eX]

[DOI]

Inf. Sci., January, 2024

Living-off-The-Land Reverse-Shell Detection by Informed Data Augmentation.

[BibT_eX]

[DOI]

CoRR, 2024

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates.

[BibT_eX]

[DOI]

CoRR, 2024

σ-zero: Gradient-based Optimization of 𝓁<sub>0</sub>-norm Adversarial Examples.

[BibT_eX]

[DOI]

CoRR, 2024

When Your AI Becomes a Target: AI Security Incidents and Best Practices.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Hardening RGB-D object recognition systems against adversarial patch attacks.

[BibT_eX]

[DOI]

Yang Zheng

Luca Demetrio

Inf. Sci., December, 2023

Machine Learning Security in Industry: A Quantitative Survey.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Forensics Secur., 2023

ImageNet-Patch: A dataset for benchmarking machine learning robustness against adversarial patches.

[BibT_eX]

[DOI]

Pattern Recognit., 2023

Stateful detection of adversarial reprogramming.

[BibT_eX]

[DOI]

Inf. Sci., 2023

Why adversarial reprogramming works, when it fails, and how to tell the difference.

[BibT_eX]

[DOI]

Inf. Sci., 2023

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning.

[BibT_eX]

[DOI]

ACM Comput. Surv., 2023

Nebula: Self-Attention for Dynamic Malware Analysis.

[BibT_eX]

[DOI]

CoRR, 2023

Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization.

[BibT_eX]

[DOI]

CoRR, 2023

Adversarial ModSecurity: Countering Adversarial SQL Injections with Robust Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2023

The Threat of Offensive AI to Organizations.

[BibT_eX]

[DOI]

Comput. Secur., 2023

Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023

Cybersecurity and AI: The PRALab Research Experience.

[BibT_eX]

[DOI]

Proceedings of the Italia Intelligenza Artificiale, 2023

AI Security and Safety: The PRALab Research Experience.

[BibT_eX]

[DOI]

Proceedings of the Italia Intelligenza Artificiale, 2023

Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning and Cybernetics, 2023

Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning and Cybernetics, 2023

Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training.

[BibT_eX]

[DOI]

Dario Lazzaro

Proceedings of the Image Analysis and Processing - ICIAP 2023, 2023

Adversarial Attacks Against Uncertainty Quantification.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors.

[BibT_eX]

[DOI]

Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security, 2023

2022

Security of Machine Learning (Dagstuhl Seminar 22281).

[BibT_eX]

[DOI]

Dagstuhl Reports, July, 2022

secml: Secure and explainable machine learning in Python.

[BibT_eX]

[DOI]

SoftwareX, 2022

Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2022

Do gradient-based explanations tell anything about adversarial robustness to android malware?

[BibT_eX]

[DOI]

Int. J. Mach. Learn. Cybern., 2022

Towards learning trustworthily, automatically, and with guarantees on graphs: An overview.

[BibT_eX]

[DOI]

Neurocomputing, 2022

FADER: Fast adversarial example rejection.

[BibT_eX]

[DOI]

Neurocomputing, 2022

Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware.

[BibT_eX]

[DOI]

Luca Demetrio

IEEE Secur. Priv., 2022

A Survey on Reinforcement Learning Security with Application to Autonomous Driving.

[BibT_eX]

[DOI]

CoRR, 2022

"Why do so?" - A Practical Perspective on Machine Learning Security.

[BibT_eX]

[DOI]

CoRR, 2022

Denial-of-Service Attack on Object Detection Model Using Universal Adversarial Perturbation.

[BibT_eX]

[DOI]

CoRR, 2022

Energy-Latency Attacks via Sponge Poisoning.

[BibT_eX]

[DOI]

CoRR, 2022

Practical Evaluation of Poisoning Attacks on Online Anomaly Detectors in Industrial Control Systems.

[BibT_eX]

[DOI]

Comput. Secur., 2022

Backdoor smoothing: Demystifying backdoor attacks on deep neural networks.

[BibT_eX]

[DOI]

Comput. Secur., 2022

Industrial practitioners' mental models of adversarial machine learning.

[BibT_eX]

[DOI]

Proceedings of the Eighteenth Symposium on Usable Privacy and Security, 2022

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Explaining Machine Learning DGA Detectors from DNS Traffic Data.

[BibT_eX]

[DOI]

Proceedings of the Italian Conference on Cybersecurity (ITASEC 2022), 2022

Robust Machine Learning for Malware Detection over Time.

[BibT_eX]

[DOI]

Proceedings of the Italian Conference on Cybersecurity (ITASEC 2022), 2022

Tessellation-Filtering ReLU Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Explainability-based Debugging of Machine Learning for Vulnerability Discovery.

[BibT_eX]

[DOI]

Angelo Sotgiu

Maura Pintor

Proceedings of the ARES 2022: The 17th International Conference on Availability, Reliability and Security, Vienna,Austria, August 23, 2022

2021

Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection.

[BibT_eX]

[DOI]

ACM Trans. Priv. Secur., 2021

Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Forensics Secur., 2021

Empirical assessment of generating adversarial configurations for software product lines.

[BibT_eX]

[DOI]

Empir. Softw. Eng., 2021

The Threat of Offensive AI to Organizations.

[BibT_eX]

[DOI]

CoRR, 2021

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples.

[BibT_eX]

[DOI]

CoRR, 2021

Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions.

[BibT_eX]

[DOI]

CoRR, 2021

secml-malware: A Python Library for Adversarial Robustness Evaluation of Windows Malware Classifiers.

[BibT_eX]

[DOI]

Luca Demetrio

CoRR, 2021

Adversarial Machine Learning: Attacks From Laboratories to the Real World.

[BibT_eX]

[DOI]

Hsiao-Ying Lin

Computer, 2021

Poisoning attacks on cyber attack detectors for industrial control systems.

[BibT_eX]

[DOI]

Moshe Kravchik

Asaf Shabtai

Proceedings of the SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, 2021

Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Slope: A First-order Approach for Measuring Gradient Obfuscation.

[BibT_eX]

[DOI]

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Complex Data: Learning Trustworthily, Automatically, and with Guarantees.

[BibT_eX]

[DOI]

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Task-Specific Automation in Deep Learning Processes.

[BibT_eX]

[DOI]

Georg Buchgeher

Gerald Czech

Adriano Souza Ribeiro

Proceedings of the Database and Expert Systems Applications - DEXA 2021 Workshops, 2021

2020

Deep neural rejection against adversarial examples.

[BibT_eX]

[DOI]

EURASIP J. Inf. Secur., 2020

Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?

[BibT_eX]

[DOI]

CoRR, 2020

Efficient Black-box Optimization of Adversarial Windows Malware with Constrained Manipulations.

[BibT_eX]

[DOI]

CoRR, 2020

Adversarial Detection of Flash Malware: Limitations and Open Issues.

[BibT_eX]

[DOI]

Comput. Secur., 2020

Poisoning Attacks on Algorithmic Fairness.

[BibT_eX]

[DOI]

David Solans

Carlos Castillo

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2019

Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection.

[BibT_eX]

[DOI]

IEEE Trans. Dependable Secur. Comput., 2019

Digital Investigation of PDF Files: Unveiling Traces of Embedded Malware.

[BibT_eX]

[DOI]

IEEE Secur. Priv., 2019

Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks.

[BibT_eX]

[DOI]

Giorgio Giacinto

ACM Comput. Surv., 2019

secml: A Python Library for Secure and Explainable Machine Learning.

[BibT_eX]

[DOI]

CoRR, 2019

Detecting Adversarial Examples through Nonlinear Dimensionality Reduction.

[BibT_eX]

[DOI]

Francesco Crecchi

Davide Bacciu

CoRR, 2019

Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks.

[BibT_eX]

[DOI]

Proceedings of the 28th USENIX Security Symposium, 2019

Towards quality assurance of software product lines with adversarial configurations.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Systems and Software Product Line Conference, 2019

Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries.

[BibT_eX]

[DOI]

Proceedings of the Third Italian Conference on Cyber Security, 2019

Detecting Black-box Adversarial Examples through Nonlinear Dimensionality Reduction.

[BibT_eX]

[DOI]

Francesco Crecchi

Davide Bacciu

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Societal Issues in Machine Learning: When Learning from Data is Not Enough.

[BibT_eX]

[DOI]

Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

Optimization and deployment of CNNs at the edge: the ALOHA experience.

[BibT_eX]

[DOI]

Ilias Theodorakopoulos

Michael Masin

Francesca Palumbo

Proceedings of the 16th ACM International Conference on Computing Frontiers, 2019

Poster: Attacking Malware Classifiers by Crafting Gradient-Attacks that Preserve Functionality.

[BibT_eX]

[DOI]

Raphael Labaca Castro

Gabi Dreo Rodosek

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019

AISec'19: 12th ACM Workshop on Artificial Intelligence and Security.

[BibT_eX]

[DOI]

Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, 2019

2018

Wild patterns: Ten years after the rise of adversarial machine learning.

[BibT_eX]

[DOI]

Pattern Recognit., 2018

Towards Robust Detection of Adversarial Infection Vectors: Lessons Learned in PDF Malware.

[BibT_eX]

[DOI]

Giorgio Giacinto

CoRR, 2018

On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks.

[BibT_eX]

[DOI]

CoRR, 2018

Towards Adversarial Configurations for Software Product Lines.

[BibT_eX]

[DOI]

CoRR, 2018

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE Symposium on Security and Privacy, 2018

Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project.

[BibT_eX]

[DOI]

Ilias Theodorakopoulos

Proceedings of the 30th International Conference on Microelectronics, 2018

Explaining Black-box Android Malware Detection.

[BibT_eX]

[DOI]

Proceedings of the 26th European Signal Processing Conference, 2018

Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables.

[BibT_eX]

[DOI]

Proceedings of the 26th European Signal Processing Conference, 2018

ALOHA: an architectural-aware framework for deep learning at the edge.

[BibT_eX]

[DOI]

Ilias Theodorakopoulos

Michael Masin

Francesca Palumbo

Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications, 2018

Session details: AI Security / Adversarial Machine Learning.

[BibT_eX]

[DOI]

Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security, 2018

11th International Workshop on Artificial Intelligence and Security (AISec 2018).

[BibT_eX]

[DOI]

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018

2017

Randomized Prediction Games for Adversarial Machine Learning.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2017

Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2017

Adversarial Detection of Flash Malware: Limitations and Open Issues.

[BibT_eX]

[DOI]

CoRR, 2017

Security Evaluation of Pattern Classifiers under Attack.

[BibT_eX]

[DOI]

CoRR, 2017

Detection of Malicious Scripting Code Through Discriminant and Adversary-Aware API Analysis.

[BibT_eX]

[DOI]

Proceedings of the First Italian Conference on Cybersecurity (ITASEC17), 2017

Infinity-Norm Support Vector Machines Against Adversarial Label Contamination.

[BibT_eX]

[DOI]

Proceedings of the First Italian Conference on Cybersecurity (ITASEC17), 2017

Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

DeltaPhish: Detecting Phishing Webpages in Compromised Websites.

[BibT_eX]

[DOI]

Proceedings of the Computer Security - ESORICS 2017, 2017

Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization.

[BibT_eX]

[DOI]

Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, 2017

10th International Workshop on Artificial Intelligence and Security (AISec 2017).

[BibT_eX]

[DOI]

Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017

Deepsquatting: Learning-Based Typosquatting Detection at Deeper Domain Levels.

[BibT_eX]

[DOI]

Proceedings of the AI*IA 2017 Advances in Artificial Intelligence, 2017

2016

Adversarial Feature Selection Against Evasion Attacks.

[BibT_eX]

[DOI]

IEEE Trans. Cybern., 2016

AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack.

[BibT_eX]

[DOI]

Igino Corona

CoRR, 2016

Super-Sparse Learning in Similarity Spaces.

[BibT_eX]

[DOI]

IEEE Comput. Intell. Mag., 2016

On Security and Sparsity of Linear Classifiers for Adversarial Settings.

[BibT_eX]

[DOI]

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2016

Who Are You? A Statistical Approach to Measuring User Authenticity.

[BibT_eX]

[DOI]

Proceedings of the 23rd Annual Network and Distributed System Security Symposium, 2016

Machine Learning under Attack: Vulnerability Exploitation and Security Measures.

[BibT_eX]

[DOI]

Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, 2016

Secure Kernel Machines against Evasion Attacks.

[BibT_eX]

[DOI]

Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security, 2016

Detecting Misuse of Google Cloud Messaging in Android Badware.

[BibT_eX]

[DOI]

Proceedings of the 6th Workshop on Security and Privacy in Smartphones and Mobile Devices, 2016

2015

Anti-spoofing, Multimodal.

[BibT_eX]

[DOI]

Gian Luca Marcialis

Proceedings of the Encyclopedia of Biometrics, Second Edition, 2015

Adversarial Biometric Recognition : A review on biometric system security from the adversarial machine-learning perspective.

[BibT_eX]

[DOI]

IEEE Signal Process. Mag., 2015

Data-driven journal meta-ranking in business and management.

[BibT_eX]

[DOI]

Gianfranco Ennas

Stephanie A. C. Schuckers

Maria Chiara Di Guardo

Scientometrics, 2015

Support vector machines under adversarial label contamination.

[BibT_eX]

[DOI]

Neurocomputing, 2015

One-and-a-Half-Class Multiple Classifier Systems for Secure Learning Against Evasion Attacks at Test Time.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems - 12th International Workshop, 2015

Is Feature Selection Secure against Training Data Poisoning?

[BibT_eX]

[DOI]

Proceedings of the 32nd International Conference on Machine Learning, 2015

Fast Image Classification with Reduced Multiclass Support Vector Machines.

[BibT_eX]

[DOI]

Proceedings of the Image Analysis and Processing - ICIAP 2015, 2015

Super-Sparse Regression for Fast Age Estimation from Faces at Test Time.

[BibT_eX]

[DOI]

Proceedings of the Image Analysis and Processing - ICIAP 2015, 2015

Sparse support faces.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Biometrics, 2015

2014

Multimodal Anti-spoofing in Biometric Recognition Systems.

[BibT_eX]

[DOI]

Proceedings of the Handbook of Biometric Anti-Spoofing, 2014

Security Evaluation of PatternClassifiers under Attack.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2014

Pattern Recognition Systems under Attack: Design Issues and Research Challenges.

[BibT_eX]

[DOI]

Int. J. Pattern Recognit. Artif. Intell., 2014

Security Evaluation of Support Vector Machines in Adversarial Environments.

[BibT_eX]

[DOI]

Igino Corona

Benjamin I. P. Rubinstein

CoRR, 2014

Poisoning Complete-Linkage Hierarchical Clustering.

[BibT_eX]

[DOI]

Eyasu Zemene Mequanint

Marcello Pelillo

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2014

Poisoning behavioral malware clustering.

[BibT_eX]

[DOI]

Konrad Rieck

Davide Ariu

Christian Wressnegger

Igino Corona

Giorgio Giacinto

Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop, 2014

On learning and recognition of secure patterns.

[BibT_eX]

[DOI]

Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop, 2014

2013

Evasion Attacks against Machine Learning at Test Time.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Poisoning attacks to compromise face templates.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Biometrics, 2013

Pattern Recognition Systems under Attack.

[BibT_eX]

[DOI]

Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2013

Is data clustering in adversarial settings secure?

[BibT_eX]

[DOI]

Proceedings of the AISec'13, 2013

2012

Security evaluation of biometric authentication systems under real spoofing attacks.

[BibT_eX]

[DOI]

IET Biom., 2012

Poisoning Adaptive Biometric Systems.

[BibT_eX]

[DOI]

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2012

Poisoning Attacks against Support Vector Machines.

[BibT_eX]

[DOI]

Proceedings of the 29th International Conference on Machine Learning, 2012

Learning sparse kernel machines with biometric similarity functions for identity recognition.

[BibT_eX]

[DOI]

Proceedings of the IEEE Fifth International Conference on Biometrics: Theory, 2012

2011

A survey and experimental evaluation of image spam filtering techniques.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2011

Microbagging Estimators: An Ensemble Approach to Distance-weighted Classifiers.

[BibT_eX]

[DOI]

Proceedings of the 3rd Asian Conference on Machine Learning, 2011

Support Vector Machines Under Adversarial Label Noise.

[BibT_eX]

[DOI]

Proceedings of the 3rd Asian Conference on Machine Learning, 2011

Design of robust classifiers for adversarial environments.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Systems, 2011

Bagging Classifiers for Fighting Poisoning Attacks in Adversarial Classification Tasks.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems - 10th International Workshop, 2011

Robustness of multi-modal biometric verification systems under realistic spoofing attacks.

[BibT_eX]

[DOI]

Proceedings of the 2011 IEEE International Joint Conference on Biometrics, 2011

Understanding the risk factors of learning in adversarial environments.

[BibT_eX]

[DOI]

Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, 2011

Robustness of multi-modal biometric systems under realistic spoof attacks against all traits.

[BibT_eX]

[DOI]

Proceedings of the IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, 2011

2010

Multiple classifier systems for robust classifier design in adversarial environments.

[BibT_eX]

[DOI]

Int. J. Mach. Learn. Cybern., 2010

Multiple Classifier Systems under Attack.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

2009

Evade Hard Multiple Classifier Systems.

[BibT_eX]

[DOI]

Proceedings of the Applications of Supervised and Unsupervised Ensemble Methods, 2009

Bayesian Linear Combination of Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Innovations in Neural Information Paradigms and Applications, 2009

Multiple Classifier Systems for Adversarial Classification Tasks.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

2008

Adversarial Pattern Classification Using Multiple Classifiers and Randomisation.

[BibT_eX]

[DOI]

Proceedings of the Structural, 2008

Improving Image Spam Filtering Using Image Text Features.

[BibT_eX]

[DOI]

Proceedings of the CEAS 2008, 2008

2007

Bayesian Analysis of Linear Combiners.

[BibT_eX]

[DOI]