We propose AvatarVTON, the first 4D virtual try-on framework that generates realistic try-on results from a single in-shop garment image, enabling free pose control, novel-view rendering, and diverse garment choices. Unlike existing methods, AvatarVTON supports dynamic garment interactions under single-view supervision, without relying on multi-view garment captures or physics priors. The framework consists of two key modules: (1) a Reciprocal Flow Rectifier, an optical-flow-based correction strategy without external priors that stabilizes avatar fitting and ensures temporal coherence; and (2) a Non-Linear Deformer, which decomposes Gaussian maps into view-pose-invariant and view-pose-specific components, enabling adaptive, non-linear garment deformations. To establish a benchmark for 4D virtual try-on, we extend existing baselines with unified modules for fair qualitative and quantitative comparisons. Extensive experiments show that AvatarVTON achieves high fidelity, diversity, and dynamic garment realism, making it well-suited for AR/VR, gaming, and digital-human applications.

Identifying the source platform of an online video is a critical yet challenging task in digital forensics, complicated by proprietary transcoding and adversarial post-processing. The preceding forensic methods are limited to a single modality, analyzing either fragile container metadata or easily distorted spatiotemporal artifacts severely compromise their robustness. This paper pioneers a multi-modal framework that deeply integrates both information domains. We contend that naive fusion is insufficient as it fails to address cross-modal ambiguity-instances where modalities provide conflicting evidence. To resolve this, we propose DAFN, a novel Dual-modality Ambiguity-Aware Fingerprinting Network which extract features from both container structure and video content. At its core, DAFN introduces an adaptive fusion mechanism guided by a measure of cross-modal ambiguity. This mechanism, which incorporates a variational module to quantify the discrepancy between modalities, enables the model to intelligently arbitrate between evidence sources. We further contribute CNSNVD, a large-scale dataset with nine major platforms and six post-processing types. Extensive experiments show that DAFN significantly outperforms existing baselines, establishing a new state-of-the-art by effectively resolving modal ambiguity to achieve superior accuracy and resilience.

Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) have noticeably advanced photo-realistic novel view synthesis using images from densely spaced camera viewpoints. However, these methods struggle in few-shot scenarios due to limited supervision. In this paper, we present NexusGS, a 3DGS-based approach that enhances novel view synthesis from sparse-view images by directly embedding depth information into point clouds, without relying on complex manual regularizations. Exploiting the inherent epipolar geometry of 3DGS, our method introduces a novel point cloud densification strategy that initializes 3DGS with a dense point cloud, reducing randomness in point placement while preventing over-smoothing and overfitting. Specifically, NexusGS comprises three key steps: Epipolar Depth Nexus, Flow-Resilient Depth Blending, and Flow-Filtered Depth Pruning. These steps leverage optical flow and camera poses to compute accurate depth maps, while mitigating the inaccuracies often associated with optical flow. By incorporating epipolar depth priors, NexusGS ensures reliable dense point cloud coverage and supports stable 3DGS training under sparse-view conditions. Experiments demonstrate that NexusGS significantly enhances depth accuracy and rendering quality, surpassing state-of-the-art methods by a considerable margin. Furthermore, we validate the superiority of our generated point clouds by substantially boosting the performance of competing methods. Project page: https://usmizuki.github.io/NexusGS/.

Image Sharing Chain Detection (ISCD) aims to reconstruct the complete trajectory of an image's dissemination across social platforms and is an important task in multimedia forensics. Current methods using DCT histograms are insufficient in uncovering platform compression traces and exhibit limitations in detecting weak trace platforms. In this letter, we propose an innovative dual-stream ISCD framework via dynamic information compensation. This framework integrates features from both the frequency domain and the residual domain to extract compression characteristics. Unlike existing methods, we employ binary stereo DCT in the frequency domain to focus on the spatiality of compression operations. Additionally, we design a dynamic information compensation mechanism to enhance platform traces by storing compensation fingerprints of the sharing chains. Furthermore, we develop a new dataset, F-4OSN-SC, encompassing 4 platforms to simulate more realistic social networking scenarios. Experimental results demonstrate that our model outperforms existing methods across multiple datasets.

With the development of new-generation mobile communication technology, the Internet of Vehicles is playing an increasingly important role in people’s lives. However, the sensitive information contained in its “data fingerprinting” raises many privacy and security concerns. To better protect the location privacy of users on the Internet of Vehicles, this article proposes a collaborative service-based privacy protection scheme for the Internet of Vehicles. In this scheme, each requesting user first initiates a location service query by generating a pairing index of the location points set. Then a customized pairing result threshold is used to determine the collaborating users that can participate in the service response. And in the process, the location privacy of users is secured by location point generalization and encryption. In addition, redundant location service recommendations are eliminated by the proposed repeatability tests. Security analysis and experiments show that this scheme has good performance and anti-privacy leakages, forgery attacks, collusion attacks, etc.

Community detection is of great help to understand the structures and functions of complex networks. It has become one of popular research topics in the field of complex networks analysis. Due to the simplicity, flexibility, effectiveness and better interpretability, Nonnegative Matrix Factorization (NMF)-based methods have been widely employed for community detection. However, most existing NMF-based community detection methods are linear and their performance is limited when facing networks with diversified structure information. In view of this, we propose a nonlinear NMF-based method named NMFGAAE, which is composed of two main modules: NMF and Graph Attention Auto-Encoder (GAAE). This approach can boost the performance of NMF-based community detection methods by the aid of graph neural networks and deep clustering. More specifically, GAAE introduces an attention mechanism directed by NMF-based community detection to learn the node representations, while NMF can simultaneously factor these representations to uncover the community structure. We design a unified framework to jointly optimize GAAE and NMF modules, which is very beneficial to obtain better community detection results. We conduct extensive experiments on synthetic and real-world networks. The results show that our NMFGAAE not only performs better than state-of-the-art NMF-based community detection methods, but also outperforms some network representation based baselines. More importantly, NMFGAAE indeed can boost the performance of NMF-based community detection methods.