Imbalanced data distribution is a common challenge that hinders model performance across various industries, particularly due to the scarcity of faulty samples. Sample generation can help address this issue, but existing methods often overlook causal structures and dependencies, resulting in low-quality synthetic samples. Hence, a novel generative method, Causal-Enhanced Latent Space Wasserstein Generative Adversarial Network (CE-LSWGAN) with gradient penalty, is proposed for imbalanced discrete integer data. First, a causal directed acyclic graph is extracted using the Peter-Clark (PC) algorithm. The causal dependencies are then embedded into the latent space of a Variational Autoencoder (VAE) through graph propagation, creating a constrained structural causal prior. Next, a Generative Adversarial Network (GAN) is introduced in this structured latent space to train the latent variables of encoder. A gradient penalty is applied to ensure stable training. To further enhance the generation of minority class samples, a dynamic resampling strategy based on class weights is employed. Finally, within the VAE decoder, the GAN-generated latent variables are integrated with causal constraints, ensuring that generated samples adhere to causal logic. Experimental results demonstrate that CE-LSWGAN achieves an average causal consistency of 0.6088 and improves similarity between generated and original data by 18.27% compared to six state-of-the-art baselines. Downstream classifiers show substantial performance gains, with the F1-score increasing by up to 60% compared to the baseline models without data augmentation. The model attains precision and recall scores of 0.9351 and 0.9352, respectively. Overall, CE-LSWGAN demonstrates superior performance in generation quality, causal fidelity, and task adaptability, providing a reliable data augmentation framework for the causal-based applications.

In recent years, the rapid development of Internet technology has constantly enriched people’s daily life and gradually changed from the traditional computer terminal to the mobile terminal. But with it comes the security problems brought by the mobile terminal. Especially for Android system, due to its open source nature, malicious applications continue to emerge, which greatly threatens the data security of users. Therefore, this paper proposes a method of trusted embedded static measurement and data transmission protection architecture based on Android to reduce the risk of data leakage in the process of terminal storage and transmission. We conducted detailed data and feasibility analysis of the proposed method from the aspects of time consumption, storage overhead and security. The experimental results show that this method can detect Android system layer attacks such as self-booting of the malicious module and improve the security of data encryption and transmission process effectively. Compared with the native system, the additional performance overhead is small.

Aiming at the problem of poor tracking robustness caused by severe occlusion, deformation, and object rotation of deep learning object tracking algorithm in complex scenes, an improved deep reinforcement learning object tracking algorithm based on actor-double critic network is proposed. In offline training phase, the actor network moves the rectangular box representing the object location according to the input sequence image to obtain the action value, that is, the horizontal, vertical, and scale transformation of the object. Then, the designed double critic network is used to evaluate the action value, and the output double Q value is averaged to guide the actor network to optimize the tracking strategy. The design of double critic network effectively improves the stability and convergence, especially in challenging scenes such as object occlusion, and the tracking performance is significantly improved. In online tracking phase, the well-trained actor network is used to infer the changing action of the bounding box, directly causing the tracker to move the box to the object position in the current frame. Several comparative tracking experiments were conducted on the OTB100 visual tracker benchmark and the experimental results show that more intensive reward settings significantly increase the actor network’s output probability of positive actions. This makes the tracking algorithm proposed in this paper outperforms the mainstream deep reinforcement learning tracking algorithms and deep learning tracking algorithms under the challenging attributes such as occlusion, deformation, and rotation.