Real-World Adversarial Attack

Real-World Adversarial Attack refers to adversarial attacks implemented on machine learning models in practical application scenarios, with the aim of causing the model to produce incorrect predictions or decisions through carefully crafted input samples. The goal is to evaluate and enhance the robustness and security of the model when facing real-world threats. The application value of this task lies in helping researchers and developers identify potential vulnerabilities in the model, optimize defense mechanisms, and ensure that the system operates stably and reliably in complex and dynamic environments.