Fetching the paper…
Reading the bibliography…
We present a new type of backdoor attack that exploits a vulnerability of convolutional neural networks (CNNs) that has been previously unstudied.
Rootkits: subverting the Windows kernel
G. Hoglund and J. Butler · 2006
Earlier work this paper cites.
Subvirt: Implementing malware with virtual machines
S. T. King and P. M. Chen · 2006
Earlier work this paper cites.
Practical variational inference for neural networks
A. Graves · 2011
Earlier work this paper cites.
Deep neural networks segment neuronal membranes in electron microscopy images
D. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber · 2012
Earlier work this paper cites.
SMM rootkit: a new breed of OS independent malware
S. Embleton, S. Sparks, and C. C. Zou · 2013
Earlier work this paper cites.
Speech recognition with deep recurrent neural networks
A. Graves, A.-R. Mohamed, and G. Hinton · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Earlier work this paper cites.
A disaster foretold — and ignored
C. Timberg · 2015
Earlier work this paper cites.
Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Y. Gal and Z. Ghahramani · 2016
Earlier work this paper cites.
Deep learning
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio · 2016
Cited alongside, same era.
Cleverhans v0.1: an adversarial machine learning library
I. J. Goodfellow, N. Papernot, and P. D. McDaniel · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
A uror: defending against poisoning attacks in collaborative deep learning systems
S. Shen, S. Tople, and P. Saxena · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 2016
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
LOTS about attacking deep features
A. Rozsa, M. Günther, and T. E. Boult · 2017
Later among the works it cites.
A survey of stealth malware: Attacks, mitigation measures, and steps toward autonomous open world solutions
E. Rudd, A. Rozsa, M. Gunther, and T. Boult · 2017
Later among the works it cites.
Adversarial generative nets: Neural network attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2017
Later among the works it cites.
On the detection of kernel-level rootkits using hardware performance counters
B. Singh, D. Evtyushkin, J. Elwell, R. Riley, and I. Cervesato · 2017
Later among the works it cites.
VGGFace2: A dataset for recognising faces across pose and age
Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
X. Chen, C. Liu, B. Li, K. Lu, and D. Song · 2017
Cited alongside, same era.
The evolution of process hiding techniques in malware-current threats and possible countermeasures
S. Eresheim, R. Luh, and S. Schrittwieser · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
T. Gu, B. Dolan-Gavitt, and S. Garg · 2017
Cited alongside, same era.
Delving into transferable adversarial examples and black-box attacks
Y. Liu, X. Chen, C. Liu, and D. Song · 2017
Cited alongside, same era.
Universal adversarial perturbations
S.-M. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
Cited alongside, same era.
I. Evtimov, K. Eykholt, E. Fernandes, T. Kohno, B. Li, A. Prakash, A. Rahmati, and D. Song · 2018
Closest in time.
Mnist cnn
Keras Team · 2018
Closest in time.
Fine-pruning: Defending against backdooring attacks on deep neural networks
K. Liu, B. Dolan-Gavitt, and S. Garg · 2018
Closest in time.
VGGFace implementation with keras framework
R. C. Malli · 2018
Closest in time.
Fooling vision and language models despite localization and attention mechanism
X. Xu, X. Chen, C. Liu, A. Rohrbach, T. Darrell, and D. Song · 2018
Closest in time.