Exploring Targeted Misclassification Attacks: Leveraging GradCam for Image Manipulation and Label Misclassification
سال انتشار: 1402
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 90
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شناسه ملی سند علمی:
ITCT20_081
تاریخ نمایه سازی: 5 مهر 1402
چکیده مقاله:
This research paper conducts a comprehensive investigation into the susceptibility of deep learning models to adversarial attacks, specifically focusing on targeted misclassification attacks and their implications for applications utilizing these models. The paper initially provides an overview of deep learning models, highlighting their significance across various domains, and then delves into the concept of adversarial attacks, emphasizing their ability to manipulate deep learning models and compromise their reliability. The study explores targeted misclassification attacks in-depth, discussing their motivations and potential consequences for deep learning-based applications. To assess the impact of targeted misclassification attacks, the paper employs the GradCam method, which enables the modification of images based on the GradCam of the desired target class. By adopting this approach, the study aims to reveal the vulnerability of deep learning models to targeted misclassification attacks, offering insights into potential defense mechanisms and underscoring the importance of safeguarding deep learning-based applications against evolving adversarial threats. The experimental results demonstrate the effectiveness of the proposed approach, achieving a favorable average fooling ratio of ۰.۷۰ and an average rate of ۰.۳۶ for adversarial confidence drop in generating deceptive adversarial samples.
کلیدواژه ها:
نویسندگان
Pouya Ardehkhani
Dept. Computer Engineering, Faculty of Engineering, College of Farabi, University of Tehran Iran
Pegah Ardehkhani
Department of Industrial Engineering, Sharif University of Technology Iran
Amirreza Mokhtari Rad
Dept. Computer Engineering, Faculty of Engineering, College of Farabi, University of Tehran Iran