Existing photorealistic style transfer methods are broadly categorized into two groups: image-guided and text-guided approaches. The image-guided paradigm requires a reference style image, which is superior when the target style is difficult to define precisely with text. Unfortunately, such references are not always available in practical scenarios. In contrast, the text-guided paradigm offers greater flexibility. However, existing text-guided methods often fail to preserve the details of content images or perform poorly when content images deviate from normal style. In this paper, we present a novel multimodal-guided photorealistic style transfer framework, supporting flexible switching and fusion of both modalities while ensuring robust performance across content images in arbitrary styles. Specifically, we adopt a two-stage pipeline. First, the Style Removal Module removes the original style from the content image. Then, the Style Injection Module applies stylization based on the style guidance (image, text, or their fusion). To make the text-guided branch compatible with this pipeline, we propose the Image-Assisted Textual Style Injection (IATSI) strategy. Additionally, we design a Dual-Residual Adaptive MLP (DRA-MLP), which exhibits strong color mapping capability and avoids spatial distortions. Extensive experiments show that our method achieves state-of-the-art (SOTA) performance in both image-guided and text-guided settings. Moreover, we innovatively implement multimodal fusion-guided photorealistic style transfer, achieving promising results.

Graph Neural Networks (GNNs) have emerged as powerful tools for processing graph-structured data, enabling applications in various domains. Yet, GNNs are vulnerable to model extraction attacks, imposing risks to intellectual property. To mitigate model extraction attacks, model ownership verification is considered an effective method. However, throughout a series of empirical studies, we found that the existing GNN ownership verification methods either mandate unrealistic conditions or present unsatisfactory accuracy under the most practical settings—the black-box setting where the verifier only requires access to the final output (e.g., posterior probability) of the target model and the suspect model.Inspired by the studies, we propose a new, black-box GNN ownership verification method that involves local independent models and shadow surrogate models to train a classifier for performing ownership verification. Our method boosts the verification accuracy by exploiting two insights: (1) We consider the overall behaviors of the target model for decision-making, better utilizing its holistic fingerprinting; (2) We enrich the fingerprinting of the target model by masking a subset of features of its training data, injecting extra information to facilitate ownership verification.To assess the effectiveness of our proposed method, we perform an intensive series of evaluations with 5 popular datasets, 5 mainstream GNN architectures, and 16 different settings. Our method achieves nearly perfect accuracy with a marginal impact on the target model in all cases, significantly outperforming the existing methods and enlarging their practicality. We also demonstrate that our method maintains robustness against adversarial attempts to evade the verification.