Manipulation of Artifical Intelligence in Image Based Data: Adversarial Examples Techniques

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Emsal Aynaci Altinay Utku Kose


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.

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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: <>. Date accessed: 04 oct. 2023.
Natural Sciences - Regular Research Paper


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