Towards Evaluating the Robustness of Neural Networks

Carlini, Nicholas, Wagner, David·2017·2017 IEEE Symposium on Security and Privacy (SP)

Cited by (1,019)

Items 1,001–1,019 of 1,019

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  6. Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
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  7. Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
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  8. Adversarial Examples Are Not Bugs, They Are Features
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  9. Black-box Adversarial Attacks with Limited Queries and Information
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  10. Analyzing Federated Learning through an Adversarial Lens
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  11. Detecting Adversarial Samples from Artifacts
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  12. Adversarial training for free
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  14. Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
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  15. Untitled work
    2017 · arXiv (Cornell University)
  16. Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
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  17. Adversarial objects against lidar-based autonomous driving systems
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  18. Untitled work
    2018 · Neural Information Processing Systems
  19. Security and Privacy Issues in Deep Learning
    Bae, Ho, Jang, Jaehee, Jung, Dahuin, Jang, Hyemi, Ha, Heonseok, Lee, Hyungyu, Yoon, Sungroh · 2018 · arXiv