Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
ISSTA		ISSTA
1805.05010.pdf		1805.05010.pdf
1809.01266v2.pdf		1809.01266v2.pdf
ADVERSARIAL EXAMPLES IN THE PHYSICAL WORLD.pdf		ADVERSARIAL EXAMPLES IN THE PHYSICAL WORLD.pdf
Adversarial Examples.ppt		Adversarial Examples.ppt
Adversarial Transformation Networks Learning to Generate Adversarial Examples.pdf		Adversarial Transformation Networks Learning to Generate Adversarial Examples.pdf
Automated Directed Fairness Testing.pdf		Automated Directed Fairness Testing.pdf
Concolic Testing for Deep Neural Networks.pdf		Concolic Testing for Deep Neural Networks.pdf
DeepFool a simple and accurate method to fool deep neural networks.pdf		DeepFool a simple and accurate method to fool deep neural networks.pdf
DeepGauge Multi-Granularity Testing Criteria for Deep.pdf		DeepGauge Multi-Granularity Testing Criteria for Deep.pdf
DeepRoad GAN-based Metamorphic Autonomous Driving System Testing.pdf		DeepRoad GAN-based Metamorphic Autonomous Driving System Testing.pdf
DeepTest Automated Testing of.pdf		DeepTest Automated Testing of.pdf
Deepxplore Automated whitebox testing of deep learning systems.pdf		Deepxplore Automated whitebox testing of deep learning systems.pdf
EXPLAINING AND HARNESSING Adversarial examples.pdf		EXPLAINING AND HARNESSING Adversarial examples.pdf
Feature-Guided Black-Box Safety Testing of Deep.pdf		Feature-Guided Black-Box Safety Testing of Deep.pdf
Geometric robustness of deep networks analysis and improvement.pdf		Geometric robustness of deep networks analysis and improvement.pdf
Houdini Fooling Deep Structured Prediction Models.pdf		Houdini Fooling Deep Structured Prediction Models.pdf
Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing.pdf		Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing.pdf
LICENSE		LICENSE
One pixel attack for fooling deep neural networks.pdf		One pixel attack for fooling deep neural networks.pdf
README.md		README.md
Semantically Enhanced Software Traceability Using Deep Learning Techniques.pdf		Semantically Enhanced Software Traceability Using Deep Learning Techniques.pdf
Simple Black-Box Adversarial Perturbations for Deep Networks.pdf		Simple Black-Box Adversarial Perturbations for Deep Networks.pdf
Symbolic Execution for Deep Neural Networks.pdf		Symbolic Execution for Deep Neural Networks.pdf
TensorFuzz Debugging Neural Networks with.pdf		TensorFuzz Debugging Neural Networks with.pdf
Testing Deep Neural Networks.pdf		Testing Deep Neural Networks.pdf
Testing Deep Neural Networks.ppt		Testing Deep Neural Networks.ppt
Testing Testing Deep Neural Networks		Testing Testing Deep Neural Networks
Testing and Validating Machine Learning Classifiers by.pdf		Testing and Validating Machine Learning Classifiers by.pdf
The Limitations of Deep Learning in Adversarial Settings.pdf		The Limitations of Deep Learning in Adversarial Settings.pdf
The Mythos of Model Interpretability.pdf		The Mythos of Model Interpretability.pdf
Towards Evaluating the Robustness of Neural Networks.pdf		Towards Evaluating the Robustness of Neural Networks.pdf
Transferability in Machine Learning from Phenomena to Black Box Attacks.pdf		Transferability in Machine Learning from Phenomena to Black Box Attacks.pdf
Universal adversarial perturbations.pdf		Universal adversarial perturbations.pdf
Validating a Deep Learning.pdf		Validating a Deep Learning.pdf
ase18main-p519-p.pdf		ase18main-p519-p.pdf

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