In the domain of photorealistic talking head generation, the fidelity of audio-driven lip motion synthesis is essential for realistic virtual interactions. Existing methods face two key challenges: a lack of vivacity due to limited diversity in generated lip poses and noticeable anamorphose motions caused by poor temporal coherence. To address these issues, we propose LawD-Net, a novel deep-learning architecture enhancing lip synthesis through a Local Affine Warping Deformation mechanism. This mechanism models the intricate lip movements in response to the audio input by controllable non-linear warping fields. These fields consist of local affine transformations focused on abstract keypoints within deep feature maps, offering a novel universal paradigm for feature warping in networks. Additionally, LawDNet incorporates a dual-stream discriminator for improved frame-to-frame continuity and employs face normalization techniques to handle pose and scene variations. Extensive evaluations demonstrate LawDNet’s superior robustness and lip movement dynamism performance compared to previous methods.

Anti-money laundering (AML) is crucial to maintaining national financial security. Contemporary AML methods focus on homogeneous mining or unitary money laundering pattern. These methods ignore a characteristic of gang operation in money laundering. Thus, in this paper, we propose a multi-view graph-based hierarchical representation learning method, named MG-HRL, to mine organized money laundering groups. In particular, we extract multi-level representations of transaction subgraphs, including transaction features, user features, structural features, and high-order association features from multiple observational perspectives. To learn the correlation between users, we model transaction networks as heterogeneous information networks (HINs) and design six meta-paths related to money laundering scenarios to mine correlations among users. Combining with correlation representations of users, we propose a heterogeneous hypergraph representation learning method to learn high-order representations of transaction subgraphs. Through hierarchical representation learning, the MG-HRL achieves full exploration of money laundering groups. Finally, we conduct experiments on two public transaction datasets. The result shows that MG-HRL method performs better than other state-of-the-art baselines.

Camouflaged targets are a type of nonsalient target with high foreground and background fusion and minimal target feature information, making target recognition extremely difficult. Most detection algorithms for camouflaged targets use only the target’s single-band information, resulting in low detection accuracy and a high missed detection rate. We present a multimodal image fusion camouflaged target detection technique (MIF-YOLOv5) in this paper. First, we provide a multimodal image input to achieve pixel-level fusion of the camouflaged target’s optical and infrared images to improve the effective feature information of the camouflaged target. Second, a loss function is created, and the K -Means++ clustering technique is used to optimize the target anchor frame in the dataset to increase camouflage personnel detection accuracy and robustness. Finally, a comprehensive detection index of camouflaged targets is proposed to compare the overall effectiveness of various approaches. More crucially, we create a multispectral camouflage target dataset to test the suggested technique. Experimental results show that the proposed method has the best comprehensive detection performance, with a detection accuracy of 96.5%, a recognition probability of 92.5%, a parameter number increase of 1×10 4 , a theoretical calculation amount increase of 0.03 GFLOPs, and a comprehensive detection index of 0.85. The advantage of this method in terms of detection accuracy is also apparent in performance comparisons with other target algorithms.

Realistic virtual humans play a crucial role in numerous industries, such as metaverse, intelligent healthcare, and self-driving simulation. But creating them on a large scale with high levels of realism remains a challenge. The utilization of deep implicit function sparks a new era of image-based 3D clothed human reconstruction, enabling pixel-aligned shape recovery with fine details. Subsequently, the vast majority of works locate the surface by regressing the deterministic implicit value for each point. However, should all points be treated equally regardless of their proximity to the surface? In this paper, we propose replacing the implicit value with an adaptive uncertainty distribution, to differentiate between points based on their distance to the surface. This simple "value ⇒ distribution" transition yields significant improvements on nearly all the baselines. Furthermore, qualitative results demonstrate that the models trained using our uncertainty distribution loss, can capture more intricate wrinkles, and realistic limbs. Code and models are available for research purposes at github.com/psyai-net/D-IF release.

For the future open 5G Internet of Vehicles (IoV), due to the flexibility and load sharing, the popular network architecture of IoV proposed by many studies is the mobile-edge computing (MEC) framework combining with software-defined networking (SDN). However, under this architecture, moving vehicles and MEC devices are not like the cloud SDN with strong security protection. Thus, identity verification is an important security issue. We find that if the identity credentials of vehicles and infrastructures are obtained by adversaries (i.e., identity theft), the current cryptography-based authentication methods cannot cope with this problem. In this article, we propose a behavior-based verification method, named Crowd-Learning, by utilizing the idea of crowd in software-defined vehicular networks with a MEC framework. In Crowd-Learning, we design an incentive mechanism to stimulate some MEC infrastructures to provide accurate and appropriate amount of data for future correct behavior estimation. Without knowing the model of the dynamic environment, this incentive mechanism needs to apply reinforcement learning to let MEC infrastructures learn how to send data based on the current state. Our Crowd-Learning method verifies vehicles and reduces the verification latency by estimating the vehicle’s behavior in advance. Meanwhile, it verifies infrastructures during the process of reinforcement learning based on the idea of crowd intelligence. The fake infrastructures and anomalous vehicles expose themselves when learning. In experiments, we use the traffic simulation tool, called simulation of urban mobility (SUMO), to generate extensive vehicle traces and evaluate the performance of the Crowd-Learning verification method. The results show that the Crowd-Learning verification method can ensure high verification accuracy for vehicles and infrastructures with satisfying low verification latency.