Adversarial examples are input samples that are imperceptible to humans but can fool deep neural networks. These examples pose significant risks for safety-critical applications of deep learning. This paper reviews recent findings on adversarial examples, summarizes methods for generating them, and proposes a taxonomy of these methods. It also investigates applications of adversarial examples and explores countermeasures and challenges. Deep learning has achieved significant progress in various applications, including image classification, object recognition, speech recognition, and more. However, deep neural networks are vulnerable to adversarial examples, which can be generated with minimal perturbations. These examples can be used to attack models in safety-critical environments, such as autonomous vehicles and speech recognition systems. Adversarial examples can be generated using various methods, including L-BFGS, Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Jacobian-based Saliency Map Attack (JSMA), DeepFool, CPPN EA Fool, C&W's Attack, Zeroth Order Optimization (ZOO), Universal Perturbation, One Pixel Attack, Feature Adversary, Hot/Cold, Natural GAN, Model-based Ensembling Attack, and Ground-Truth Attack. These methods vary in their approach, perturbation scope, and constraints. Adversarial examples have been applied in various tasks, including reinforcement learning, generative modeling, face recognition, object detection, semantic segmentation, natural language processing, and malware detection. Countermeasures for adversarial examples include robustness evaluation, regularization, and model ensembling. The paper also discusses the challenges and potential solutions for adversarial examples, including transferability, existence, and robustness evaluation. It concludes that adversarial examples are a critical issue for deep learning, and further research is needed to improve the robustness and security of deep neural networks.Adversarial examples are input samples that are imperceptible to humans but can fool deep neural networks. These examples pose significant risks for safety-critical applications of deep learning. This paper reviews recent findings on adversarial examples, summarizes methods for generating them, and proposes a taxonomy of these methods. It also investigates applications of adversarial examples and explores countermeasures and challenges. Deep learning has achieved significant progress in various applications, including image classification, object recognition, speech recognition, and more. However, deep neural networks are vulnerable to adversarial examples, which can be generated with minimal perturbations. These examples can be used to attack models in safety-critical environments, such as autonomous vehicles and speech recognition systems. Adversarial examples can be generated using various methods, including L-BFGS, Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), Jacobian-based Saliency Map Attack (JSMA), DeepFool, CPPN EA Fool, C&W's Attack, Zeroth Order Optimization (ZOO), Universal Perturbation, One Pixel Attack, Feature Adversary, Hot/Cold, Natural GAN, Model-based Ensembling Attack, and Ground-Truth Attack. These methods vary in their approach, perturbation scope, and constraints. Adversarial examples have been applied in various tasks, including reinforcement learning, generative modeling, face recognition, object detection, semantic segmentation, natural language processing, and malware detection. Countermeasures for adversarial examples include robustness evaluation, regularization, and model ensembling. The paper also discusses the challenges and potential solutions for adversarial examples, including transferability, existence, and robustness evaluation. It concludes that adversarial examples are a critical issue for deep learning, and further research is needed to improve the robustness and security of deep neural networks.