Gradient Based Adversarial
Gradient-based adversarial attacks exploit the vulnerabilities of machine learning models by generating subtly perturbed inputs that cause misclassification. Current research focuses on improving the efficiency and effectiveness of these attacks across various model architectures, including large language models, graph neural networks, and image classifiers, often leveraging techniques like gradient estimation and optimization algorithms such as projected gradient descent and momentum iterative FGSM. This research is crucial for enhancing the robustness and security of machine learning systems in diverse applications, ranging from image recognition and natural language processing to critical infrastructure and cybersecurity. Understanding and mitigating these attacks is vital for ensuring the reliability and trustworthiness of AI systems.