Vision-language pretraining (VLP) models have demonstrated outstanding performance in image-text understanding tasks but remain highly susceptible to transferable adversarial attacks. While ensemble-based guided attacks improve adversarial transferability by increasing the diversity of image-text pairs, they primarily rely on spatial-domain data augmentation, which can lead to model overfitting to image details and limit the generalization capability of attacks. To address this limitation, this study proposes a frequency-domain adjustment-based adversarial attack method that modifies specific frequency components of input images to reduce detail interference and enhance the stability of adversarial examples. Additionally, a fine-grained feature extraction technique is introduced to optimize image-text alignment, further improving the transferability of cross-modal attacks. Experimental results demonstrate that the proposed method achieves superior attack transferability and generalization performance across two major VLP architectures, fusion models and alignment models, as well as multiple tasks on the Flickr30 K and MSCOCO datasets.

The growth analysis of enterprises is an important basis for predicting the future development trend of enterprises. For an enterprise itself, the enterprise growth analysis can help the enterprise to understand its own business situation. It can also assist the enterprise to accurately customize the development strategy. As far as the investment market is concerned, the enterprise growth analysis can help investors comprehensively understand the investment target and reduce the investment risk as well as improve the investment benefit. This paper makes a comparative analysis on the growth of 4937 enterprises with all A-shares in different industries from seven dimensions, including competitiveness, profitability, operation ability, debt paying ability, R & D ability, scale expansion ability, enterprise supply chain ability. This paper reveals that there are significant differences in the growth of enterprises in different industries in the post-epidemic era.

Enterprises loss is a growth strategy, in which enterprises migrate across regions/cities to adapt to the changes of internal and external environment, in this way to seek new development space and further reach the growth again. As the carrier of local economic development, the transfer of enterprises from one region to another undoubtedly means the loss of regional resources for the region. This paper takes large- scale enterprises as the research object. Then, this paper uses questionnaire data and statistical data, and adopts the combination of PCA algorithm and extreme value standardization method to comprehensively evaluate the loss probability of enterprises. This method will reflect the loss tendency of enterprises in the region, and make an empirical analysis on the large-scale enterprises in region, in this way to help regional managers have an early insight into the loss tendency of enterprises in the region. Finally, it will provide a reference for stabilizing the regional economy and help reduce the loss risk of large-scale enterprises in the region.