Recently, deep learning systems have been widely utilized in various fields, prompting increased attention to their security. Fuzz testing is a crucial automated testing method; however, traditional approaches are not directly applicable to the testing of deep neural networks (DNNs). In light of this challenge, this study proposes a DNN fuzz testing method based on gradient weighted class activation graphs. By integrating model visualization interpretation technology and Grad-CAM technology, only significant areas are disrupted to rapidly generate test cases capable of inducing DNN errors. Additionally, high-quality initial seeds are selected based on the heat map to assess the degree of image feature distinctiveness. Adversarial perturbations are then exclusively applied to areas with high heat values in order to enhance the authenticity of the generated images. Experimental results demonstrate that this approach effectively enhances model robustness and accuracy, produces high-quality test cases, and significantly contributes to model repair efforts.