Deep learning vulnerability analysis against adversarial attacks

Abstract

In the age of artificial intelligence advancements, deep learning models are essential for applications ranging from image recognition to natural language processing. Despite their capabilities, they're vulnerable to adversarial examplesdeliberately modified inputs to cause errors. This paper explores these vulnerabilities, attributing them to the complexity of neural networks, the diversity of training data, and the training methodologies. It demonstrates how these aspects contribute to the models' susceptibility to adversarial attacks. Through case studies and empirical evidence, the paper highlights instances where advanced models were misled, showcasing the challenges in defending against these threats. It also critically evaluates mitigation strategies, including adversarial training and regularization, assessing their efficacy and limitations. The study underlines the importance of developing AI systems that are not only intelligent but also robust against adversarial tactics, aiming to enhance future deep learning models' resilience to such vulnerabilities.