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Deep neural networks are vulnerable to a range of adversaries.
Tracking down software bugs using automatic anomaly detection
Sudheendra Hangal and Monica S Lam · 2002
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Anomaly detection using call stack information
Henry Hanping Feng, Oleg M Kolesnikov, Prahlad Fogla, Wenke Lee, and Weibo Gong · 2003
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On the relevance of code anomalies for identifying architecture degradation symptoms
Isela Macia, Roberta Arcoverde, Alessandro Garcia, Christina Chavez, and Arndt von Staa · 2012
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Intriguing properties of neural networks, 2013
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Explaining and harnessing adversarial examples, 2014
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Code completion with statistical language models
Veselin Raychev, Martin Vechev, and Eran Yahav · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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A convolutional attention network for extreme summarization of source code
Miltiadis Allamanis, Hao Peng, and Charles A. Sutton · 2016
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Deepfool: A simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Targeted backdoor attacks on deep learning systems using data poisoning, 2017
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Hotflip: White-box adversarial examples for text classification, 2017
Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou · 2017
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A survey of machine learning for big code and naturalness
Miltiadis Allamanis, Earl T Barr, Premkumar Devanbu, and Charles Sutton · 2018
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code2seq: Generating sequences from structured representations of code
Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav · 2018
Cited alongside, same era.
Satisfiability modulo theories
Clark Barrett and Cesare Tinelli · 2018
Cited alongside, same era.
Jbmc: A bounded model checking tool for verifying java bytecode
Lucas Cordeiro, Pascal Kesseli, Daniel Kroening, Peter Schrammel, and Marek Trtik · 2018
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Sever: A robust meta-algorithm for stochastic optimization, 2018
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart · 2018
Cited alongside, same era.
Deep learning type inference
Vincent J Hellendoorn, Christian Bird, Earl T Barr, and Miltiadis Allamanis · 2018
Cited alongside, same era.
Fine-pruning: Defending against backdooring attacks on deep neural networks
Codesearchnet challenge: Evaluating the state of semantic code search, 2019
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 2019
Later among the works it cites.
Hidden trigger backdoor attacks, 2019
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash · 2019
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Label-consistent backdoor attacks, 2019
Alexander Turner, Dimitris Tsipras, and Aleksander Madry · 2019
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Universal adversarial triggers for attacking and analyzing nlp
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Zhao · 2019
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Coset: A benchmark for evaluating neural program embeddings, 2019
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Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2018
Cited alongside, same era.
Did the model understand the question?
Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere · 2018
Cited alongside, same era.
Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
Cited alongside, same era.
Spectral signatures in backdoor attacks
Brandon Tran, Jerry Li, and Aleksander Madry · 2018
Cited alongside, same era.
code2vec: Learning distributed representations of code
Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav · 2019
Cited alongside, same era.
On evaluating adversarial robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, and Aleksander Madry · 2019
Cited alongside, same era.
Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks
Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar · 2019
Cited alongside, same era.
Ke Wang and Mihai Christodorescu · 2019
Later among the works it cites.
Latent backdoor attacks on deep neural networks
Yuanshun Yao, Huiying Li, Haitao Zheng, and Ben Y. Zhao · 2019
Later among the works it cites.
Adversarial examples for models of code
Noam Yefet, Uri Alon, and Eran Yahav · 2019
Later among the works it cites.
Adversarial attacks on deep learning models in natural language processing: A survey
Wei Emma Zhang, Quan Z Sheng, AHOUD Alhazmi, and CHENLIANG LI · 2019
Later among the works it cites.
Using large-scale anomaly detection on code to improve kotlin compiler
Timofey Bryksin, Victor Petukhov, Ilya Alexin, Stanislav Prikhodko, Alexey Shpilman, Vladimir Kovalenko, and Nikita Povarov · 2020
Closest in time.
Badnl: Backdoor attacks against nlp models, 2020
Xiaoyi Chen, Ahmed Salem, Michael Backes, Shiqing Ma, and Yang Zhang · 2020
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Bae: Bert-based adversarial examples for text classification, 2020
Siddhant Garg and Goutham Ramakrishnan · 2020
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Semantic robustness of models of source code, 2020
Goutham Ramakrishnan, Jordan Henkel, Zi Wang, Aws Albarghouthi, Somesh Jha, and Thomas Reps · 2020
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Exploring backdoor poisoning attacks against malware classifiers
Giorgio Severi, Jim Meyer, Scott E. Coull, and Alina Oprea · 2020
Closest in time.