Universal Adversarial Perturbations

Moosavi-Dezfooli, Seyed-Mohsen, Fawzi, Alhussein, Fawzi, Omar, Frossard, Pascal·2017·2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr.2017.17

Cited by (1,005)

Items 301–400 of 1,005

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  2. Imitation Attacks and Defenses for Black-box Machine Translation Systems
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  3. Practical Adversarial Attacks Against Speaker Recognition Systems
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  4. Optical Adversarial Attack
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  6. Adversarial Machine Learning: A Multilayer Review of the State-of-the-Art and Challenges for Wireless and Mobile Systems
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  9. A Comprehensive Study on the Robustness of Deep Learning-Based Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking
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  10. Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial Perturbations.
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  11. Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges
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  12. Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks
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  13. Hardware Trojan Attacks on Neural Networks
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  14. POBA-GA: Perturbation optimized black-box adversarial attacks via genetic algorithm
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  15. RDA: Robust Domain Adaptation via Fourier Adversarial Attacking
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  16. Defending against Model Stealing via Verifying Embedded External Features
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  19. Security risks and countermeasures of adversarial attacks on AI-driven applications in 6G networks: A survey
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  20. Adversarial tensor factorization for context-aware recommendation
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  21. Efficient Adversarial Attacks for Visual Object Tracking
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  23. Selective Audio Adversarial Example in Evasion Attack on Speech Recognition System
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  24. Targeted Mismatch Adversarial Attack: Query With a Flower to Retrieve the Tower
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  25. ADGAN: Protect Your Location Privacy in Camera Data of Auto-Driving Vehicles
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  31. On Success and Simplicity: A Second Look at Transferable Targeted Attacks
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  32. Adversarial Unlearning of Backdoors via Implicit Hypergradient
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  33. RAP: Robustness-Aware Perturbations for Defending against Backdoor Attacks on NLP Models
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  34. Learning Privacy Preserving Encodings Through Adversarial Training
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  35. Unlearnable Examples: Making Personal Data Unexploitable
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  36. CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems
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  37. Trust Region Based Adversarial Attack on Neural Networks
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  43. Tiny noise, big mistakes: adversarial perturbations induce errors in brain–computer interface spellers
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  44. A Backdoor Attack against 3D Point Cloud Classifiers
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  45. An Approximated Gradient Sign Method Using Differential Evolution for Black-Box Adversarial Attack
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  46. Attacking convolutional neural network using differential evolution
    Jiawei Su, Danilo Vasconcellos Vargas, Kouichi Sakurai · 2019 · IPSJ Transactions on Computer Vision and Applications
  47. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
    Jia, Jinyuan, Ahmed Salem, Michael Backes A, Y Victoria Zhang, Neil Zhenqiang Gong · 2019 · arXiv (Cornell University)
  48. AdvDoor: adversarial backdoor attack of deep learning system
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  55. AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
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  57. Are Image-Agnostic Universal Adversarial Perturbations for Face Recognition Difficult to Detect?
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  58. Fooling a Real Car with Adversarial Traffic Signs
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  59. Measuring Robustness in Deep Learning Based Compressive Sensing
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  60. Adversarial examples for replay attacks against CNN-based face recognition with anti-spoofing capability
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  62. Towards a Robust Deep Neural Network against Adversarial Texts: A Survey
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  68. SmartBox: Benchmarking Adversarial Detection and Mitigation Algorithms for Face Recognition
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  69. Universal Adversarial Perturbations Generative Network For Speaker Recognition
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  70. Stealthy Black-Box Attacks on Deep Learning Non-Intrusive Load Monitoring Models
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  71. Role of Spatial Context in Adversarial Robustness for Object Detection
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  72. Adversarial Attack Against Urban Scene Segmentation for Autonomous Vehicles
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  73. Adversarial attacks and defenses in deep learning for image recognition: A survey
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  84. Tiny noise, big mistakes : adversarial perturbations induce errors in brain-computer interface spellers
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