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              <text>Manasa, Sanyasi; Kumar, Kukatlapalli Pradeep</text>
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              <text>DIGITAL FORENSICS ANALYSIS FOR ADVERSARIAL ATTACKS IN AI-BASED SYSTEMS</text>
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              <text>01-01-2025</text>
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              <text>Attacks on Artificial Intelligence: The New Facets of Cyber Ecospace;pp.51-70</text>
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              <text>&lt;a href="https://www.scopus.com/pages/publications/105018274596?origin=resultslist" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/pages/publications/105018274596?origin=resultslist&lt;/a&gt;</text>
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              <text>Manasa P., Department of Computer Science Engineering, Christ University, Bengaluru, India; Kumar K.P., Department of Computer Science Engineering, Christ University, Bengaluru, India</text>
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              <text>Digital forensics investigation is a field of science that focuses primarily on the recovery and examination of devices. The digital forensics or cyber forensics was primarily used for computer-based forensics. Artificial intel ligence (AI for short) is a group of computational models that use the black box-based technology of the neural network (NN) such as classification, prediction, and optimization tasks for various applications, such as computer vision, medical imaging, natural language processing, and autonomous vehicles. Cyber Forensics such as robotics, deals with a class of problems in which computational models are manipulated due to intentional malfunction such as adversarial attacks, back-end kind of attacks. This study is aimed to investigate attacks on AI. It also describes conceptual attack strategies using AI and digital forensics tools. The main purpose of this work is to provide a comprehensive analysis of malicious AI-based attacks and different types of classifiers. Two main factors were considered when testing attacks on AI-based systems: (1) Use AI to create inputs that reveal different attacks, or (2) Adjust attacker-specific attacks from different threat models and parameters.  2026 by Apple Academic Press, Inc.</text>
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              <text>Apple Academic Press</text>
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              <text>ISBN: 978-104009235-4; 978-177491622-3;</text>
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              <text>Restricted Access; Hardcopy may be available in the library</text>
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