## 1 Introduction  
With recent advances in artificial intelligence (AI), several scholars have begun to invest in deep learning research. AI represents the use of mathematical methods to enable computers to mimic human intelligence by performing inferential, predictive, perceptive, and social tasks. AI has previously exhibited varying degrees of success in image recognition [1], voice recognition [2], natural language processing [3], expert systems [4], and automatic planning [5]. A common, everyday example of AI is the automatic driving system, which encompasses image recognition, object detection, target recognition, semantic segmentation, and other imaging technologies [6]. Owing to the exceptional performance achieved by deep learning and AI, many deep learning models have recently been developed [7, 8]. The defining characteristic of deep learning is the use of an artificial neural network architecture to perform training and then accomplish prediction, classification, identification, and other tasks. The architecture of a neural network resembles that of the human brain, wherein it comprises neurons. These architectures are highly diverse, with examples including the multilayer perceptron(MLP) [9], feedforward neural network (FF) [10], recurrent neural network [11], long short-term memory [11], autoencoders (AE) [12], variational AE (VAE) [13], automatic noise reduction encoders (Denoising, DAE), convolutional neural networks (CNN) [14], and generative adversarial networks (GANs) [15]. GANS, which are typically used to generate images, were proposed by [PERSON] in 2014 [15], representing a breakthrough in the learning of unsupervised neural networks and rapidly becoming a prevalent research topic. The current applications of GANs include style transfer [16], as well as mammography, which is used to generate highly accurate digital breast images [17]. Although deep learning has an overwhelmingly positive impact on data classification, detection, and analysis, two challenging factors frequently manifest in the anomaly detection task: the insufficient quantity of normal and abnormal data during the training phase. These issues are difficult to address in the context of supervision learning. The two-stream neural network proposed by [PERSON] et al. [18] performs a two-stage model learning strategy. First, a lightweight convolutional neural network is employed on resource-constrained IoT devices to classify events as normal or abnormal. Second, the abnormal data is through bi-directional long short-term memory (BD-LSTM) for furthering their respective anomaly classification. A GAN and AE were adopted in this study to learn the form of the data latent distribution to generate a new dataset. Thus, the increase and variety of data are expected to improve the model's anomaly detection performance. The proposed method, which integrates the Wasserstein-GAN (WGAN) and Skip-GANomaly models to distinguish between normal and abnormal images, is called the Improved Wasserstein Skip-Connection GAN (IWGAN). In the experimental stage, we evaluated different hyper-parameters--including the activation function, learning rate, decay rate, training times of discriminator, and method of label smoothing--to identify the optimal combination. Consequently, the IWGAN can generate high-quality images, thereby increasing the success rate of abnormal image detection. The contributions of this study are as follows:  
* The proposed IWGAN model, which combines WGAN and Skip-GANomaly, resolves the issues posed by training difficulty and model collapse. * We optimized the training parameters of IWGAN, such as the LeakyReLU activation layer, decay learning rate, training times, and label smoothing. * The proposed model was evaluated using the Frechet Inception Distance (FID) and Area Under Curve (AUC) values. The experimental results indicate superior performance to that of existing models, such as U-Net, GAN, WGAN, GANomaly, and Skip-GANomaly. The remainder of this paper is organized as follows. Section 2 serves as a summary of the related work.