Towards Evaluating the Robustness of Neural Networks

Carlini, Nicholas, Wagner, David·2017·2017 IEEE Symposium on Security and Privacy (SP)
DOI 10.1109/sp.2017.49

Cited by (1,019)

Items 301–400 of 1,019

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  3. SHIELD
    Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E. Kounavis, Duen Horng Chau · 2018 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
  4. MagNet and "Efficient Defenses Against Adversarial Attacks" are Not Robust to Adversarial Examples
    Nicholas Carlini, David Wagner · 2017 · arXiv (Cornell University)
  5. DeepBillboard
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  7. Black-box Adversarial Attacks on Video Recognition Models
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  9. PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving
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  10. Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity
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  14. How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses
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  15. Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review
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  20. Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
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  22. Curriculum Adversarial Training
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  27. Adversarial Robustness vs. Model Compression, or Both?
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  29. Towards Robust Detection of Adversarial Examples
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  38. Adversarial Examples on Object Recognition
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  41. Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
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  43. How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey
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  44. PyTorchFI: A Runtime Perturbation Tool for DNNs
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  46. Adversarial Examples Against the Deep Learning Based Network Intrusion Detection Systems
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  47. CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples
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  48. Applications of Deep Learning in Intelligent Transportation Systems
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  49. Adversarial attacks and defenses on AI in medical imaging informatics: A survey
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  51. Towards Transferable Targeted Attack
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  52. Threat of Adversarial Attacks on DL-Based IoT Device Identification
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  53. CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
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  54. SoK: Certified Robustness for Deep Neural Networks
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