Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenizer, and then large language models (LLMs) are trained to generate the next item via sequence-to-sequence modeling. However, these two stages are optimized for different objectives: semantic reconstruction during tokenizer pretraining versus user interaction modeling during recommender training. This objective misalignment leads to two key limitations: (i) suboptimal static tokenization, where fixed token assignments fail to reflect diverse usage contexts; and (ii) discarded pretrained semantics, where pretrained knowledge—typically from language model embeddings—is overwritten during recommender training on user interactions. To address these limitations, we propose to learn DEcomposed COntextual Token Representations (DECOR), a unified framework that preserves pretrained semantics while enhancing the adaptability of token embeddings. DECOR introduces contextualized token composition to refine token embeddings based on user interaction context, and decomposed embedding fusion that integrates pretrained codebook embeddings with newly learned collaborative embeddings. Experiments on three real-world datasets demonstrate that DECOR consistently outperforms state-of-the-art baselines in recommendation performance. Our code is available at https://github.com/yliuaa/DECOR.git.

With the advancement of personalized image generation technologies, concerns about forgery attacks that infringe on portrait rights and privacy are growing. To address these concerns, protection perturbation algorithms have been developed to disrupt forgery generation. However, the protection algorithms would become ineffective when forgery attackers apply purification techniques to bypass the protection. To address this issue, we present a novel approach, Anti-Tamper Perturbation (ATP). ATP introduces a tamper-proof mechanism within the perturbation. It consists of protection and authorization perturbations, where the protection perturbation defends against forgery attacks, while the authorization perturbation detects purification-based tampering. Both protection and authorization perturbations are applied in the frequency domain under the guidance of a mask, ensuring that the protection perturbation does not disrupt the authorization perturbation. This design also enables the authorization perturbation to be distributed across all image pixels, preserving its sensitivity to purification-based tampering. ATP demonstrates its effectiveness in defending forgery attacks across various attack settings through extensive experiments, providing a robust solution for protecting individuals' portrait rights and privacy. Our code is available at: https://github.com/Seeyn/Anti-Tamper-Perturbation .

The proliferation of fake news on social media platforms disproportionately impacts vulnerable populations, eroding trust, exacerbating inequality, and amplifying harmful narratives. Detecting fake news in multimodal contexts-where deceptive content combines text and images-is particularly challenging due to the nuanced interplay between modalities. Existing multimodal fake news detection methods often emphasize cross-modal consistency but ignore the complex interactions between text and visual elements, which may complement, contradict, or independently influence the predicted veracity of a post. To address these challenges, we present Modality Interactive Mixture-of-Experts for Fake News Detection (MIMoE-FND), a novel hierarchical Mixture-of-Expert framework designed to enhance multimodal fake news detection by explicitly modeling modality interactions through an interaction gating mechanism. Our approach models modality interactions by evaluating two key aspects of modality interactions: unimodal prediction agreement and semantic alignment. The hierarchical structure of MIMoE-FND allows for distinct learning pathways tailored to different fusion scenarios, adapting to the unique characteristics of each modality interaction. By tailoring fusion strategies to diverse modality interaction scenarios, MIMoE-FND provides a more robust and nuanced approach to multimodal fake news detection. We evaluate our approach on three real-world benchmarks spanning two languages, demonstrating its superior performance compared to state-of-the-art methods. By enhancing the accuracy and interpretability of fake news detection, MIMoE-FND offers a promising tool to mitigate the spread of misinformation, with potential to better safeguard vulnerable communities against its harmful effects.

Face restoration (FR) recovers high resolution (HR) faces from low resolution (LR) faces and is challenging due to its ill-posed nature. With years of development, existing methods can produce quality HR faces with realistic details. However, we observe that key facial attributes (e.g., age and gender) of the restored faces could be dramatically different from the LR faces and call this phenomenon attribute bias, which is fatal when using FR for applications such as surveillance and security. Thus, we argue that FR should consider not only image quality as in existing works but also attribute bias. To this end, we thoroughly analyze attribute bias with extensive experiments and find that two major causes are the lack of attribute information in LR faces and bias in the training data. Moreover, we propose the DebiasFR framework to produce HR faces with high image quality and accurate facial attributes. The key design is to explicitly model the facial attributes, which also allows to adjust facial attributes for the output HR faces. Experiment results show that DebiasFR has comparable image quality but significantly smaller attribute bias when compared with state-of-the-art FR methods.

Abstract Despite impressive progress in face super‐resolution (SR), it is an open challenge to reconstruct a reliable SR face that preserves authentic facial characteristics. Here, the problem of super‐resolving low‐resolution (LR) faces to high‐resolution (HR) ones is addressed. To tackle the ill‐posed nature of face SR, the cascaded super‐resolution network (CSRNet) is proposed to utilize shape and identity priors jointly and progressively, the first to explore multiple priors. Specifically, CSRNet adopts a cascaded structure to transform an LR face to HR face progressively via multiple stages. At each stage, CSRNet forces its output face image to match both the shape priors and identity priors extracted from the ground‐truth HR face. The shape priors estimated in one stage are merged into the inputs of its subsequent stage to provide rich information for the face SR. To generate realistic yet discriminative faces, the cascaded super‐resolution generative adversarial network (CSRGAN) is also proposed to incorporate the adversarial loss and identification loss into CSRNet. Extensive experiments on popular benchmarks show that the CSRNet and CSRGAN outperform existing face SR state‐of‐the‐art methods, both quantitatively and qualitatively, and detailed ablation studies show the advantage of this method.