This paper reviews recent findings on adversarial examples for deep neural networks, summarizes methods for generating these examples, and proposes a taxonomy of these methods. It investigates applications of adversarial examples and countermeasures, and discusses challenges and potential solutions. The paper focuses on attacks against deep neural networks in safety-critical environments, where adversarial examples can be used to manipulate systems such as autonomous vehicles and speech recognition models. The authors define a threat model that considers the testing/deploying stage, the integrity of the model, and the characteristics of adversarial examples. They categorize approaches for generating adversarial examples based on threat model, perturbation, and benchmark. The paper also explores the transferability of adversarial examples, their existence, and robustness evaluation of deep neural networks.This paper reviews recent findings on adversarial examples for deep neural networks, summarizes methods for generating these examples, and proposes a taxonomy of these methods. It investigates applications of adversarial examples and countermeasures, and discusses challenges and potential solutions. The paper focuses on attacks against deep neural networks in safety-critical environments, where adversarial examples can be used to manipulate systems such as autonomous vehicles and speech recognition models. The authors define a threat model that considers the testing/deploying stage, the integrity of the model, and the characteristics of adversarial examples. They categorize approaches for generating adversarial examples based on threat model, perturbation, and benchmark. The paper also explores the transferability of adversarial examples, their existence, and robustness evaluation of deep neural networks.