Manipulation of Artifical Intelligence in Image Based Data: Adversarial Examples Techniques
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Published
Jul 15, 2021
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Emsal Aynaci Altinay
Utku Kose
http://orcid.org/0000-0002-9652-6415
http://orcid.org/0000-0002-9652-6415
Abstract
Artificial intelligence systems are widely used in all fields of life. While the solutions of artificial intelligence have had phenomenal success there is also a dangerous side employing efforts to design attack techniques against Artificial Intelligence and its sub-field, machine learning. Thanks to such techniques, intelligent systems can be fooled to cause misclassified output results. While artificial intelligence builds the future of humanity on intelligent systems, it also has future concerns as various applications from self-driving cars, disease detection to security will be done with autonomous intelligent systems without human beings. Moving from explanations, in this thesis study, artificial intelligence is manipulated by using adversarial examples techniques in image-based data. Adversarial examples are defined as training data that can deceive a machine learning technique into misleading it about the target problem, resulting in a failed or malicious intelligent system. Machine learning models are not robust to adversarial examples. This study shows how artificial intelligence systems are deceived by applying current adversarial example techniques. Obtained results show that the applied techniques provide sufficient attack success rates.
Article Details
How to Cite
AYNACI ALTINAY, Emsal; KOSE, Utku.
Manipulation of Artifical Intelligence in Image Based Data: Adversarial Examples Techniques.
Journal of Multidisciplinary Developments, [S.l.], v. 6, n. 1, p. 8-17, july 2021.
ISSN 2564-6095.
Available at: <http://www.jomude.com/index.php/jomude/article/view/88>. Date accessed: 21 jan. 2025.
Section
Natural Sciences - Regular Research Paper
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[3] Pavaloiu, A., & Kose, U. (2017). Ethical artificial intelligence-an open question. arXiv preprint arXiv:1706.03021.
[4] Kose, U. (2017). Development of artificial intelligence based optimization algorithms. PhD. Thesis. Selcuk University, Institute of Natural Sciences, Dept. of Computer Engineering.
[5] Deperlioglu, O., Kose, U., Gupta, D., Khanna, A., & Sangaiah, A. K. (2020). Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network. Computer Communications, 162, 31-50.
[6] Neill, Daniel B. (2013). Using Artificial Intelligence to Improve Hospital Inpatient Care. IEE Computer Society.
[7] Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T. (2017). Artificial intelligence in precision cardiovascular medicine. J Am Coll Cardiol. 2017; 69:2657–64.
[8] Walton-Rivers, J., Williams, P.R., Bartle, R., Perez-Liebana D., Lucas, S.M. (2017). Evaluating and modelling hanabi-playing agents, 2017 IEEE Congress on Evolutionary Computation (CEC).
[9] Rajpurkar, P., Irvin, J., Zhu, K. (2017). CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. 05225
[10] Pan, Y.H. (2016). Heading toward artificial intelligence 2.0. Engineering, 2(4), 409–413. http://dx.doi.org/10.1016/J.ENG.2016.04.018.
[11] McFarland, M. (2017). Google uses AI to help diagnose breast cancer, Erişim Tarihi:1.04.2021.http://money.cnn.com/2017/03/03/technology/google-breast-cancer-ai/.
[12] Cheung, C.W., Tsang I.T., Wong, K.H. (2017). Robot Avatar: A Virtual Tourism Robot for People With Disabilities. International Journal of Computer Theory And Engineering, Singapore, (9)3, 229-234.
[13] Johnson, K.W., Soto, J.T., Glicksberg, B.S., Shameer, K., Miotto, R., Ali, M., Ashley, E., Dudley, J.T. (2018). Artificial Intelligence in Cardiology. Journal of the American College of Cardiology, 71(23), 2668-2679.
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[15] Vassev, E. (2016). Safe artificial intelligence and formal methods. In International Symposium on Leveraging Applications of Formal Methods (pp. 704-713). Springer, Cham.
[16] Yampolskiy, R. V. (2016). Taxonomy of pathways to dangerous artificial intelligence. In Workshops at the Thirtieth AAAI Conference on Artificial Intelligence.
[17] Köse, U. (2018). Are we safe enough in the future of artificial intelligence? A discussion on machine ethics and artificial intelligence safety. BRAIN. Broad Research in Artificial Intelligence and Neuroscience, 9(2), 184-197.
[18] Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2014). Intriguing properties of neural networks. 6199.
[19] Goodfellow, I. J., Shlens, J., Szegedy, C. (2015). Explaining and harnessing adversarial examples. 6572.
[20] Hu, W., Tan, Y. (2017). Generating adversarial malware examples for black-box attacks based on GAN.05983.
[21] Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M. K. (2016). Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 24-28 October, Vienna, 1528-1540.
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[23] Liang, B., Li, H., Su, M., Bian, P., Li, X., Shi, W. (2017). Deep text classification can be fooled. 08006.
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[27] Ebrahimi, J., Lowd, D., Dou, D. (2018). On Adversarial Examples for Character-Level Neural Machine Translation. 09030.
[28] Carlini, N., Wagner, D. (2018). Audio adversarial examples: Targeted attacks on speech to-text. In 2018 IEEE Security and Privacy Workshops (SPW) (pp. 1-7). IEEE.
[29] Akhtar, N., Mian, A. (2018). Threat of adversarial attacks on deep learning in computer vision: A survey. IEEE Access, 6, 14410-14430.
[30] Ilyas, A., Engstrom, L., Athalye, A., Lin, J. (2018). Black-box adversarial attacks with limited queries and information. arXiv preprint arXiv:1804.08598.
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