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Cited by 86Adversarial Robustness in Machine LearningDigital Media Forensic DetectionSoftware Testing and Debugging Techniques

Ask, Acquire, and Attack: Data-Free UAP Generation Using Class Impressions

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Mopuri, Konda Reddy, Uppala, Phani Krishna, Babu, R. Venkatesh·2018·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-01240-3_2
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Crossref · 10.1007/978-3-030-01240-3_2

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  • Robustness, generalizability, and other vulnerability tests can prevent integrity and availability attacks and data breaches.

    “Robustness, generalizability, and other vulnerability tests (eg, black-box and white-box tests) can further prevent integrity and availability attacks and data breaches [88].”

    From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [88]

    The discussion on p. 3 says data-free approaches reveal model vulnerabilities, and §4.2 (p. 10) evaluates white-box and black-box attacks. The paper also calls for robust models against adversarial attacks (p. 2), but it does not say that vulnerability tests prevent integrity or availability attacks or data breaches. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 24

  1. [2]2. Baluja, S., Fischer, I.: Learning to attack: Adversarial transformation networks. In: Proceedings of AAAI (2018)
  2. [4]4. Biggio, B., Fumera, G., Roli, F.: Pattern recognition systems under attack: Design issues and research challenges. International Journal of Pattern Recognition and Artificial Intelligence 28 (07) (2014)
  3. [5]5. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: Delving deep into convolutional nets. In: Proceedings of the British Machine Vision Conference (BMVC) (2014)
  4. [6]6. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, (NIPS) (2014)
  5. [9]9. Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., Tygar, J.D.: Adversarial machine learning. In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence. AISec '11 (2011)
  6. [10]10. Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014) · arXiv
  7. [11]11. Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial examples in the physical world. In: International Conference on Learning Representations (ICLR) (2017)
  8. [12]12. Liu, Y., Chen, X., Liu, C., Song, D.: Delving into transferable adversarial examples and black-box attacks. In: International Conference on Learning Representations (ICLR) (2017)
  9. [14]14. Moosavi-Dezfooli, S., Fawzi, A., Frossard, P.: Deepfool: A simple and accurate method to fool deep neural networks. In: IEEE conference on Computer Vision and Pattern Recognition (CVPR) (2016)
  10. [15]15. Mopuri, K.R., Ganeshan, A., Babu, R.V.: Generalizable data-free objective for crafting universal adversarial perturbations. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)
  11. [16]16. Mopuri, K.R., Garg, U., Babu, R.V.: CNN fixations: An unraveling approach to visualize the discriminative image regions. arXiv preprint arXiv:1708.06670 (2017) · arXiv
  12. [17]17. Mopuri, K.R., Garg, U., Babu, R.V.: Fast feature fool: A data independent approach to universal adversarial perturbations. In: Proceedings of the British Machine Vision Conference (BMVC) (2017)
  13. [18]18. Mopuri, K.R., Ojha, U., Garg, U., Babu, R.V.: NAG: Network for adversary generation. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR) (2018)
  14. [20]20. Olah, C., Mordvintsev, A., Schubert, L.: Feature visualization. Distill (2017), https: //distill.pub/2017/feature-visualization
  15. [21]21. Papernot, N., McDaniel, P.D., Goodfellow, I.J., Jha, S., Celik, Z.B., Swami, A.: Practical black-box attacks against deep learning systems using adversarial examples. In: Asia Conference on Computer and Communications Security (ASIACCS) (2017)
  16. [22]22. Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015) · arXiv
  17. [23]23. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115 (3), 211-252 (2015)
  18. [24]24. Salimans, T., Goodfellow, I.J., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. In: Advances in Neural Information Processing Systems (NIPS) (2016)
  19. [25]25. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Gradcam: Visual explanations from deep networks via gradient-based localization. In: The IEEE International Conference on Computer Vision (ICCV) (2017)
  20. [27]27. Springenberg, J., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: The all convolutional net. In: International Conference on Learning Representations (ICLR) (workshop track) (2015)
  21. [28]28. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., Fergus, R.: Intriguing properties of neural networks. In: International Conference on Learning Representations (ICLR) (2013)
  22. [29]29. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision (ECCV). pp. 818-833 (2014)
  23. [30]30. Zhang, J., Lin, Z., Brandt, J., Shen, X., Sclaroff, S.: Top-down neural attention by excitation backprop. In: European Conference on Computer Vision(ECCV) (2016)
  24. [31]31. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of Computer Vision and Pattern Recognition (CVPR) (2016)

Cited by

Sorted by citations

Authors
Mopuri, K. R., Uppala, P. K., & Babu, R. V.
Affiliations
Indian Institute of Science Bangalore (IN)
Corresponding author
Mopuri, Konda Reddy
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2018
Pages
20-35
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-01240-3_2
OpenAlex
W2885039223
Cited by
86
References
5
Citations by year
2026: 2 · 2025: 6 · 2024: 13 · 2023: 13 · 2022: 17 · 2021: 16 · 2020: 17 · 2019: 1 · 2018: 1
FWCI
3.56
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
Keywords
universal adversarial perturbations, generative adversarial networks, adversarial robustness, deep learning security

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