In recent years, neural networks have revolutionized the field of artificial intelligence, exhibiting remarkable performance in various complex tasks. However, their susceptibility to adversarial examples has become a pressing concern, challenging the reliability and security of these powerful systems. Adversarial examples, which are specially crafted inputs, can deceive neural networks into generating incorrect outputs while remaining imperceptible to human observers.