Learner-Independent Targeted Data Omission Attacks

[1]  Fabio Roli,et al.  Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning , 2017, Pattern Recognit..

[2]  Blaine Nelson,et al.  Support Vector Machines Under Adversarial Label Noise , 2011, ACML.

[3]  Justin Hsu,et al.  Data Poisoning against Differentially-Private Learners: Attacks and Defenses , 2019, IJCAI.

[4]  J. Doug Tygar,et al.  Adversarial machine learning , 2019, AISec '11.

[5]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[6]  Ling Huang,et al.  ANTIDOTE: understanding and defending against poisoning of anomaly detectors , 2009, IMC '09.

[7]  Tudor Dumitras,et al.  When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks , 2018, USENIX Security Symposium.

[8]  Wenke Lee,et al.  Polymorphic Blending Attacks , 2006, USENIX Security Symposium.

[9]  Wei Cai,et al.  A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View , 2018, IEEE Access.

[10]  Ananthram Swami,et al.  The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).

[11]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[12]  Claudia Eckert,et al.  Support vector machines under adversarial label contamination , 2015, Neurocomputing.

[13]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[14]  Patrick D. McDaniel,et al.  Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples , 2016, ArXiv.

[15]  Salvatore J. Stolfo,et al.  Casting out Demons: Sanitizing Training Data for Anomaly Sensors , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

[16]  N. Altman An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[17]  Chang Liu,et al.  Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning , 2018, 2018 IEEE Symposium on Security and Privacy (SP).

[18]  Percy Liang,et al.  Certified Defenses for Data Poisoning Attacks , 2017, NIPS.

[19]  Blaine Nelson,et al.  The security of machine learning , 2010, Machine Learning.

[20]  Yevgeniy Vorobeychik,et al.  Feature Cross-Substitution in Adversarial Classification , 2014, NIPS.

[21]  Blaine Nelson,et al.  Can machine learning be secure? , 2006, ASIACCS '06.

[22]  Blaine Nelson,et al.  Exploiting Machine Learning to Subvert Your Spam Filter , 2008, LEET.

[23]  Lawrence Carin,et al.  Joint dictionary learning and topic modeling for image clustering , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  Susmita Sur-Kolay,et al.  Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare , 2015, IEEE Journal of Biomedical and Health Informatics.