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In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks.
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Abstract interpretation: A unified lattice model for static analysis of programs by construction or approximation of fixpoints
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An integrative model of organizational trust
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Extraction of rules from discrete-time recurrent neural networks
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Software unit test coverage and adequacy
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Counterexample-guided abstraction refinement
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The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W. (2000) · 2000
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A practical tutorial on modified condition/decision coverage
Hayhurst, K., Veerhusen, D., Chilenski, J., and Rierson, L. (2001) · 2001
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Rule extraction from neural networks via decision tree induction
Sato, M. and Tsukimoto, H. (2001) · 2001
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Trust and trustworthiness
Hardin, R. (2002) · 2002
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Counterexample-guided abstraction refinement for symbolic model checking
Clarke, E., Grumberg, O., Jha, S., Lu, Y., and Veith, H. (2003) · 2003
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Multiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., and Bovik, A. C. (2003) · 2003
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A Safety Framework for Critical Systems Utilising Deep Neural Networks
Zhao, X., Banks, A., Sharp, J., Robu, V., Flynn, D., Fisher, M., and Huang, X. (2020) · 2003
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Safe bounds in linear and mixed-integer linear programming
Neumaier, A. and Shcherbina, O. (2004) · 2004
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Metric Spaces
OSearcoid, M. (2006) · 2006
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Introduction to Software Testing
Ammann, P. and Offutt, J. (2008) · 2008
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Visualizing Higher-Layer Features of a Deep Network
Erhan, D., Bengio, Y., Courville, A., and Vincent, P. (2009) · 2009
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Rectified linear units improve restricted Boltzmann machines
Nair, V. and Hinton, G. E. (2010) · 2010
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An abstraction-refinement approach to verification of artificial neural networks
Pulina, L. and Tacchella, A. (2010) · 2010
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Natural language processing (almost) from scratch
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., and Kuksa, P. (2011) · 2011
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An analysis and survey of the development of mutation testing
Jia, Y. and Harman, M. (2011) · 2011
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Do-178c, software considerations in airborne systems and equipment certification
RTCA (2011) · 2011
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A unifying view on dataset shift in classification
Moreno-Torres, J. G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N. V., and Herrera, F. (2012) · 2012
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Evasion attacks against machine learning at test time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., and Roli, F. (2013) · 2013
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Repair with on-the-fly program analysis
Könighofer, R. and Bloem, R. (2013) · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A. (2013) · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. A. (2014) · 2014
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014) · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R. (2014) · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W. (2015) · 2015
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Inverting convolutional networks with convolutional networks
Dosovitskiy, A. and Brox, T. (2015) · 2015
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Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., and Dean, J. (2015) · 2015
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Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A. (2015) · 2015
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Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. (2015) · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
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The interpretation and evaluation of assurance cases
Rushby, J. (2015) · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L. (2015) · 2015
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Schaul, T., Quan, J., Antonoglou, I., and Silver, D. (2015) · 2015
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Deep reinforcement learning with double q-learning
van Hasselt, H., Guez, A., and Silver, D. (2015) · 2015
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Understanding neural networks through deep visualization
Yosinski, J., Clune, J., Nguyen, A. M., Fuchs, T. J., and Lipson, H. (2015) · 2015
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General data protection regulation. http://data.europa.eu/eli/reg/2016/679/oj
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Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D. (2016) · 2016
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Measuring neural net robustness with constraints
Bastani, O., Ioannou, Y., Lampropoulos, L., Vytiniotis, D., Nori, A., and Criminisi, A. (2016) · 2016
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Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Early methods for detecting adversarial images
Hendrycks, D. and Gimpel, K. (2016) · 2016
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Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., and Fei-Fei, L. (2016) · 2016
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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I. J., and Bengio, S. (2016) · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P. (2016) · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A. (2016a) · 2016
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Distillation as a defense to adversarial perturbations against deep neural networks
Papernot, N., McDaniel, P., Wu, X., Jha, S., and Swami, A. (2016b) · 2016
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The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P. D., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A. (2016c) · 2016
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”why should I trust you?”: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
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Grad-cam: Why did you say that? visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D. (2016) · 2016
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Conditional image generation with pixelcnn decoders
van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., and Kavukcuoglu, K. (2016) · 2016
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Dueling network architectures for deep reinforcement learning
Wang, Z., de Freitas, N., and Lanctot, M. (2016) · 2016
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Deepred – rule extraction from deep neural networks
Zilke, J. R., Loza Mencía, E., and Janssen, F. (2016) · 2016
Cited alongside, same era.
Dimensionality reduction as a defense against evasion attacks on machine learning classifiers
Bhagoji, A. N., Cullina, D., and Mittal, P. (2017) · 2017
Cited alongside, same era.
An entropy based approach for ssim speed up
Bruni, V. and Vitulano, D. (2017) · 2017
Cited alongside, same era.
Piecewise linear neural network verification: A comparative study
Stochastic activation pruning for robust adversarial defense
Dhillon, G. S., Azizzadenesheli, K., Lipton, Z. C., Bernstein, J., Kossaifi, J., Khanna, A., and Anandkumar, A. (2018) · 2018
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Output range analysis for deep neural networks
Dutta, S., Jha, S., Sanakaranarayanan, S., and Tiwari, A. (2018) · 2018
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A dual approach to scalable verification of deep networks
Dvijotham, K., Stanforth, R., Gowal, S., Mann, T. A., and Kohli, P. (2018) · 2018
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Hotflip: White-box adversarial examples for text classification
Ebrahimi, J., Rao, A., Lowd, D., and Dou, D. (2018) · 2018
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Adversarial attacks against medical deep learning systems
Finlayson, S. G., Kohane, I. S., and Beam, A. L. (2018) · 2018
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Bunel, R., Turkaslan, I., Torr, P. H., Kohli, P., and Kumar, M. P. (2017) · 2017
Cited alongside, same era.
Maximum resilience of artificial neural networks
Cheng, C.-H., Nührenberg, G., and Ruess, H. (2017) · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y. (2017) · 2017
Cited alongside, same era.
Systematic testing of convolutional neural networks for autonomous driving
Dreossi, T., Ghosh, S., Sangiovanni-Vincentelli, A., and Seshia, S. A. (2017) · 2017
Cited alongside, same era.
Formal verification of piece-wise linear feed-forward neural networks
Ehlers, R. (2017) · 2017
Cited alongside, same era.
A rotation and a translation suffice: Fooling cnns with simple transformations
Engstrom, L., Tsipras, D., Schmidt, L., and Madry, A. (2017) · 2017
Cited alongside, same era.
Detecting Adversarial Samples from Artifacts
Feinman, R., Curtin, R. R., Shintre, S., and Gardner, A. B. (2017) · 2017
Cited alongside, same era.
AI2: Safety and robustness certification of neural networks with abstract interpretation
Gehr, T., Mirman, M., Drachsler-Cohen, D., Tsankov, P., Chaudhuri, S., and Vechev, M. (2018) · 2018
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Estimating Information Flow in Neural Networks
Goldfeld, Z., van den Berg, E., Greenewald, K., Melnyk, I., Nguyen, N., Kingsbury, B., and Polyanskiy, Y. (2018) · 2018
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Gradient masking causes CLEVER to overestimate adversarial perturbation size
Goodfellow, I. J. (2018) · 2018
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Symbolic execution for deep neural networks
Gopinath, D., Wang, K., Zhang, M., Pasareanu, C. S., and Khurshid, S. (2018) · 2018
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DLFuzz: Differential fuzzing testing of deep learning systems
Guo, J., Jiang, Y., Zhao, Y., Chen, Q., and Sun, J. (2018) · 2018
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Learning universal adversarial perturbations with generative models
Hayes, J. and Danezis, G. (2018) · 2018
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Analyzing deep neural networks with symbolic propagation: Towards higher precision and faster verification
Li, J., Liu, J., Yang, P., Chen, L., and Huang, X. (2018) · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R. (2018) · 2018
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The mythos of model interpretability
Lipton, Z. C. (2018) · 2018
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Differentiable abstract interpretation for provably robust neural networks
Mirman, M., Gehr, T., and Vechev, M. (2018) · 2018
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Generalizable data-free objective for crafting universal adversarial perturbations
Mopuri, K. R., Ganeshan, A., and Babu, R. V. (2018) · 2018
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Cascade adversarial machine learning regularized with a unified embedding
Na, T., Ko, J. H., and Mukhopadhyay, S. (2018) · 2018
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Formal analysis of deep binarized neural networks
Narodytska, N. (2018) · 2018
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Verifying properties of binarized deep neural networks
Narodytska, N., Kasiviswanathan, S. P., Ryzhyk, L., Sagiv, M., and Walsh, T. (2018) · 2018
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TensorFuzz: Debugging neural networks with coverage-guided fuzzing
Odena, A. and Goodfellow, I. (2018) · 2018
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Technical report on the cleverhans v2.1.0 adversarial examples library
Papernot, N., Faghri, F., Carlini, N., Goodfellow, I., Feinman, R., Kurakin, A., Xie, C., Sharma, Y., Brown, T., Roy, A., Matyasko, A., Behzadan, V., Hambardzumyan, K., Zhang, Z., Juang, Y.-L., Li, Z., Sheatsley, R., Garg, A., Uesato, J., Gierke, W., Dong, Y., Berthelot, D., Hendricks, P., Rauber, J., and Long, R. (2018) · 2018
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Robust deep reinforcement learning with adversarial attacks
Pattanaik, A., Tang, Z., Liu, S., Bommannan, G., and Chowdhary, G. (2018) · 2018
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Generative adversarial perturbations
Poursaeed, O., Katsman, I., Gao, B., and Belongie, S. J. (2018) · 2018
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Certified defenses against adversarial examples
Raghunathan, A., Steinhardt, J., and Liang, P. (2018) · 2018
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Anchors: High-precision model-agnostic explanations
Ribeiro, M. T., Singh, S., and Guestrin, C. (2018) · 2018
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Salay, R. and Czarnecki, K. (2018) · 2018
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
Samangouei, P., Kabkab, M., and Chellappa, R. (2018) · 2018
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On the information bottleneck theory of deep learning
Saxe, A. M., Bansal, Y., Dapello, J., Advani, M., Kolchinsky, A., Tracey, B. D., and Cox, D. D. (2018) · 2018
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MuNN: Mutation analysis of neural networks
Shen, W., Wan, J., and Chen, Z. (2018) · 2018
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Autonomous systems - an architectural characterization
Sifakis, J. (2018) · 2018
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Certifiable distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J. (2018) · 2018
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Song, Y., Kim, T., Nowozin, S., Ermon, S., and Kushman, N. (2018) · 2018
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DeepTest: Automated testing of deep-neural-network-driven autonomous cars
Tian, Y., Pei, K., Jana, S., and Ray, B. (2018) · 2018
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Ensemble Adversarial Training: Attacks and Defenses
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P. (2018) · 2018
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Sequential attacks on agents for long-term adversarial goals
Tretschk, E., Oh, S. J., and Fritz, M. (2018) · 2018
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Automated directed fairness testing
Udeshi, S., Arora, P., and Chattopadhyay, S. (2018) · 2018
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Formal security analysis of neural networks using symbolic intervals
Wang, S., Pei, K., Whitehouse, J., Yang, J., and Jana, S. (2018) · 2018
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Sparse adversarial perturbations for videos
Wei, X., Zhu, J., and Su, H. (2018) · 2018
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Extracting automata from recurrent neural networks using queries and counterexamples
Weiss, G., Goldberg, Y., and Yahav, E. (2018) · 2018
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Towards fast computation of certified robustness for relu networks
Weng, T.-W., Zhang, H., Chen, H., Song, Z., Hsieh, C.-J., Boning, D., Dhillon, I. S., and Daniel, L. (2018) · 2018
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Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Weng, T.-W., Zhang, H., Chen, P.-Y., Yi, J., Su, D., Gao, Y., Hsieh, C.-J., and Daniel, L. (2018) · 2018
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Feature-guided black-box safety testing of deep neural networks
Wicker, M., Huang, X., and Kwiatkowska, M. (2018) · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Wong, E. and Kolter, Z. (2018) · 2018
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Output reachable set estimation and verification for multi-layer neural networks
Xiang, W., Tran, H.-D., and Johnson, T. T. (2018) · 2018
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Spatially transformed adversarial examples
Xiao, C., Zhu, J.-Y., Li, B., He, W., Liu, M., and Song, D. (2018) · 2018
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Coverage-guided fuzzing for deep neural networks
Xie, X., Ma, L., Juefei-Xu, F., Chen, H., Xue, M., Li, B., Liu, Y., Zhao, J., Yin, J., and See, S. (2018) · 2018
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Feature squeezing: Detecting adversarial examples in deep neural networks
Xu, W., Evans, D., and Qi, Y. (2018) · 2018
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Deepstellar: model-based quantitative analysis of stateful deep learning systems
Du, X., Xie, X., Li, Y., Ma, L., Liu, Y., and Zhao, J. (2019) · 2019
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Scalable verified training for provably robust image classification
Gowal, S., Dvijotham, K. D., Stanforth, R., Bunel, R., Qin, C., Uesato, J., Arandjelovic, R., Mann, T., and Kohli, P. (2019) · 2019
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Universal perturbation attack against image retrieval
Li, J., Ji, R., Liu, H., Hong, X., Gao, Y., and Tian, Q. (2019) · 2019
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Universal adversarial perturbations for speech recognition systems
Neekhara, P., Hussain, S., Pandey, P., Dubnov, S., McAuley, J., and Koushanfar, F. (2019) · 2019
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Global robustness evaluation of deep neural networks with provable guarantees for the Hamming distance
Ruan, W., Wu, M., Sun, Y., Huang, X., Kroening, D., and Kwiatkowska, M. (2019) · 2019
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Zhang, H., Zhang, P., and Hsieh, C.-J. (2019) · 2019
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Formal verification of robustness and resilience of learning-enabled state estimation system for robotics
Huang, W., Zhou, Y., Meng, J., Sharp, J., Maskell, S., and Huang, X. (2020) · 2020
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HyDiff: Hybrid differential software analysis
Noller, Y., Păsăreanu, C. S., Böhme, M., Sun, Y., Nguyen, H. L., and Grunske, L. (2020) · 2020
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Reliability validation of learning enabled vehicle tracking
Sun, Y., Zhou, Y., Maskell, S., Sharp, J., and Huang, X. (2020) · 2020
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A game-based approximate verification of deep neural networks with provable guarantees
Wu, M., Wicker, M., Ruan, W., Huang, X., and Kwiatkowska, M. (2020) · 2020
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