SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted EXPLORING THE TRANSFERABILITY OF ADVERSARIAL EXAMPLES IN NATURAL LANGUAGE PROCESSING(Texas A&M University-Kingsville, 2024-06-21) Allahyani, Samah; Nijim, MaisIn recent years, there has been a growing concern about the vulnerability of machine learning models, particularly in the field of natural language processing (NLP). Many tasks in natural language processing, such as text classification, machine translation, and question answering, are at risk of adversarial attacks where maliciously crafted inputs can cause them to make incorrect predictions or classifications. Adversarial examples created on one model can also fool another model. The transferability of adversarial has also garnered significant attention as it is a crucial property for facilitating black-box attacks. In our comprehensive research, we employed an array of widely used NLP models for sentiment analysis and text classification tasks. We first generated adversarial examples for a set of source models, using five state-of-the-art attack methods. We then evaluated the transferability of these adversarial examples by testing their effectiveness on different target models, to explore the main factors such as model architecture, dataset characteristics and the perturbation techniques impacting transferability. Moreover, we extended our investigation by delving into transferability-enhancing techniques. We assisted two transferability-enhancing methods and leveraged the power of Large Language Models (LLM) to generate natural adversarial examples that show a moderate transferability across different NLP architecture. Through our research, we aim to provide insights into the transferability of adversarial examples in NLP, and shed light on the factors that contribute to their transferability. This knowledge can then be used to develop more robust, and resilient, NLP models that are less susceptible to adversarial attacks; ultimately, enhancing the security and reliability of these systems in various applications.14 0Item Restricted DETECTING MANIPULATED AND ADVERSARIAL IMAGES: A COMPREHENSIVE STUDY OF REAL-WORLD APPLICATIONS(UCF STARS, 2023-11-06) Alkhowaiter, Mohammed; Zou, CliffThe great advance of communication technology comes with a rapid increase of disinformation in many kinds and shapes; manipulated images are one of the primary examples of disinformation that can affect many users. Such activity can severely impact public behavior, attitude, and be- lief or sway the viewers’ perception in any malicious or benign direction. Additionally, adversarial attacks targeting deep learning models pose a severe risk to computer vision applications. This dissertation explores ways of detecting and resisting manipulated or adversarial attack images. The first contribution evaluates perceptual hashing (pHash) algorithms for detecting image manipulation on social media platforms like Facebook and Twitter. The study demonstrates the differences in image processing between the two platforms and proposes a new approach to find the optimal detection threshold for each algorithm. The next contribution develops a new pHash authentication to detect fake imagery on social media networks, using a self-supervised learning framework and contrastive loss. In addition, a fake image sample generator is developed to cover three major image manipulating operations (copy-move, splicing, removal). The proposed authentication technique outperforms the state-of-the-art pHash methods. The third contribution addresses the challenges of adversarial attacks to deep learning models. A new adversarial-aware deep learning system is proposed using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. The proposed approach outperforms current state-of-the-art adversarial defense systems. Finally, the fourth contribution fuses big data from Extra-Military resources to support military decision-making. The study pro- poses a workflow, reviews data availability, security, privacy, and integrity challenges, and suggests solutions. A demonstration of the proposed image authentication is introduced to prevent wrong decisions and increase integrity. Overall, the dissertation provides practical solutions for detect- ing manipulated and adversarial attack images and integrates our proposed solutions in supporting military decision-making workflow.31 0