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Deep learning based systems are susceptible to adversarial attacks, where a small, imperceptible change at the input alters the model prediction.
Detecting adversarial samples using influence functions and nearest neighbors
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Learning to discriminate perturbations for blocking adversarial attacks in text classification
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A study of black box adversarial attacks in computer vision
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Context based spelling correction
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ELECTRA: pre-training text encoders as discriminators rather than generators
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Frequency-guided word substitutions for detecting textual adversarial examples
Maximilian Mozes, Pontus Stenetorp, Bennett Kleinberg, and Lewis D. Griffin. 2020 · 2004
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Adversarial examples on object recognition: A comprehensive survey
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Real-word spelling correction using google web it 3-grams
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Cifar-100 (canadian institute for advanced research)
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The BULATS online speaking test
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Microsoft COCO: common objects in context
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Intriguing properties of neural networks
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
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Deep residual learning for image recognition
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Character-level convolutional networks for text classification
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Uncertainty in deep learning
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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Adversarial perturbations against deep neural networks for malware classification
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, and Patrick D. McDaniel. 2016 · 2016
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Adversarial machine learning at scale
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio. 2016 · 2016
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Adversarial examples detection in deep networks with convolutional filter statistics
Xin Li and Fuxin Li. 2016 · 2016
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Adversarial deep learning for robust detection of binary encoded malware
Alex Huang, Abdullah Al-Dujaili, Erik Hemberg, and Una-May O’Reilly. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018 · 2018
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Textbugger: Generating adversarial text against real-world applications
Jinfeng Li, Shouling Ji, Tianyu Du, Bo Li, and Ting Wang. 2018 · 2018
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Characterizing adversarial subspaces using local intrinsic dimensionality
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Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, and Pascal Frossard. 2016 · 2016
Cited alongside, same era.
Crafting adversarial input sequences for recurrent neural networks
Nicolas Papernot, Patrick D. McDaniel, Ananthram Swami, and Richard E. Harang. 2016 · 2016
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli. 2017 · 2017
Cited alongside, same era.
Detecting adversarial samples from artifacts
Reuben Feinman, Ryan R. Curtin, Saurabh Shintre, and Andrew B. Gardner. 2017 · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
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Leveraging uncertainty information from deep neural networks for disease detection
Christian Leibig, Vaneeda Allken, M. Ayhan, Philipp Berens, and S. Wahl. 2017 · 2017
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Generic black-box end-to-end attack against rnns and other API calls based malware classifiers
Ishai Rosenberg, Asaf Shabtai, Lior Rokach, and Yuval Elovici. 2017 · 2017
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Grammatical error correction with neural reinforcement learning
Keisuke Sakaguchi, Matt Post, and Benjamin Van Durme. 2017 · 2017
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Xingjun Ma, Bo Li, Yisen Wang, Sarah M. Erfani, Sudanthi N. R. Wijewickrema, Michael E. Houle, Grant Schoenebeck, Dawn Song, and James Bailey. 2018 · 2018
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Adversarially regularising neural NLI models to integrate logical background knowledge
Pasquale Minervini and Sebastian Riedel. 2018 · 2018
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Adversarial reprogramming of sequence classification neural networks
Paarth Neekhara, Shehzeen Hussain, Shlomo Dubnov, and Farinaz Koushanfar. 2018 · 2018
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Adversarial over-sensitivity and over-stability strategies for dialogue models
Tong Niu and Mohit Bansal. 2018 · 2018
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Semantically equivalent adversarial rules for debugging NLP models
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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CARER: Contextualized affect representations for emotion recognition
Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018 · 2018
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Understanding measures of uncertainty for adversarial example detection
Lewis Smith and Yarin Gal. 2018 · 2018
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Identify susceptible locations in medical records via adversarial attacks on deep predictive models
Mengying Sun, Fengyi Tang, Jinfeng Yi, Fei Wang, and Jiayu Zhou. 2018 · 2018
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Robust machine comprehension models via adversarial training
Yicheng Wang and Mohit Bansal. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Learning to characterize adversarial subspaces
Xiaofeng Mao, Yuefeng Chen, Yuhong Li, Yuan He, and Hui Xue. 2019 · 2019
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Generating natural language adversarial examples through probability weighted word saliency
Shuhuai Ren, Yihe Deng, Kun He, and Wanxiang Che. 2019 · 2019
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Adversarial attack and defense of structured prediction models
Wenjuan Han, Liwen Zhang, Yong Jiang, and Kewei Tu. 2020 · 2020
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Universal Adversarial Attacks on Spoken Language Assessment Systems
Vyas Raina, Mark J.F. Gales, and Kate M. Knill. 2020 · 2020
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Towards improving adversarial training of nlp models
Jin Yong Yoo and Yanjun Qi. 2021 · 2021
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