Adversarial Attacks and Defenses in Deep Learning

Deep learning, a cornerstone of artificial intelligence (AI), has revolutionized a number of fields, including self-driving cars, image recognition, and intelligent medical applications. These developments have aided in the development of smart cities. That being said, new findings have shown that deep neural networks are susceptible to hostile attacks on security-critical tasks. This research paper investigates the related defensive strategies and explores the crucial problem of adversarial intrusions in deep learning. It draws attention to the adversarial attacks that deep neural networks are susceptible to, especially in high-security applications like network intrusion detection systems and radiofrequency identification. This paper presents the idea of adversarial intrusions, looks at their theoretical underpinnings, and evaluates how they affect different kinds of data, such as text, graphs, and images. The field of cybersecurity is given special attention, with attacks being categorized using adversarial examples and defensive measures being discussed. Additionally, the study introduces FIAT, a novel adversarial defense strategy based on feature-level interpretability. This paper intends to stimulate future research endeavors to guarantee the security and resilience of deep learning algorithms in real-world scenarios by offering a thorough overview of adversarial intrusions and defenses in the field of deep learning for Edge AI.