In user-generated-content (UGC) applications, non-expert users often rely on image-to-3D generative models to create 3D assets. In this context, primitive-based shape abstraction offers a promising solution for UGC scenarios by compressing high-resolution meshes into compact, editable representations. Towards this end, effective shape abstraction must therefore be structure-aware, characterized by low overlap between primitives, part-aware alignment, and primitive compactness. We present Light-SQ, a novel superquadric-based optimization framework that explicitly emphasizes structure-awareness from three aspects. (a) We introduce SDF carving to iteratively udpate the target signed distance field, discouraging overlap between primitives. (b) We propose a block-regrow-fill strategy guided by structure-aware volumetric decomposition, enabling structural partitioning to drive primitive placement. (c) We implement adaptive residual pruning based on SDF update history to surpress over-segmentation and ensure compact results. In addition, Light-SQ supports multiscale fitting, enabling localized refinement to preserve fine geometric details. To evaluate our method, we introduce 3DGen-Prim, a benchmark extending 3DGen-Bench with new metrics for both reconstruction quality and primitive-level editability. Extensive experiments demonstrate that Light-SQ enables efficient, high-fidelity, and editable shape abstraction with superquadrics for complex generated geometry, advancing the feasibility of 3D UGC creation. Project Page: https://johann.wang/Light-SQ/ .

This study explores the motivations driving Chinese consumers to share information about aquatic product safety risks related to radioactive wastewater discharge on social media, with a specific focus on the role of Artificial Intelligence Generated Content (AIGC) and prior information-seeking activities. The study examines key variables such as risk perception, outcome expectations, and the influence of AIGC literacy in shaping attitudes, social norms, and sharing efficacy. Data from 501 participants were analyzed using structural equation modeling. The findings indicate that risk evaluations and personal outcome expectations significantly motivate information sharing, while social expectations deter it. Additionally, AIGC literacy enhances both knowledge acquisition and sharing efficacy, while prior information seeking increases self-efficacy and risk perception, further fostering information sharing. These results provide critical insights into how environmental risk information is processed and disseminated on social media, emphasizing AIGC’s role in influencing this process.

In this paper, a fully integrated low-dropout regulator (LDO) using voltage-to-time conversion (VTC) technique is presented for under-1 V supply voltage application. A synchronous VTC technique is proposed using constant-current (CC) charging and discharging to achieve high loop gain. A high-gain charge pump (CP) is proposed to improve power-supply-rejection (PSR). Furthermore, an asynchronous step detection recovery technique is proposed to achieve fast transient response. A frequency-adaptive oscillator is proposed to remove the noise of the clock signal. The proposed LDO is designed in 28-nm process to achieve a droop voltage of 104 mV at load current transient of 90 mA. The proposed LDO achieves PSR of -77 dB at ILOAD=100 mA and PSR of -65 dB at ILOAD=10 mA for 1-kHz supply ripple frequency. The quiescent current is 32 µA and the peak current efficiency is 99.98%.

In recent years, video forensics has become an important issue. Video inter-frame forgery detection is a significant branch of forensics. In this paper, a new algorithm based on the consistency of velocity field is proposed to detect video inter-frame forgery (i.e., consecutive frame deletion and consecutive frame duplication). The generalized extreme studentized deviate (ESD) test is applied to identify the forgery types and locate the manipulated positions in forged videos. Experiments show the effectiveness of our algorithm.

The advanced technology and sophisticated software have rendered audiovisual content exposed to forgery, inspiring the emergence of multimedia forensic research. Since video tampering may involve double compression, the analysis of compression history is of significance. In this paper, we consider the processing chains of two compression steps and propose an algorithm that aims at identifying the quantization parameter used in the previous coding process. The method relies on the fact that characteristic footprints can be observed under different relationships between quantization parameters of consecutive compression operations. Features are extracted from both Discrete Cosine Transform (DCT) coefficients and their differential counterparts to capture the statistical disturbance. Experimental results demonstrate the effectiveness of our method.

With the spread of powerful and easy-to-use video editing software, digital videos are exposed to various forms of tampering. Nowadays, a considerable proportion of surveillance systems and video cameras have built-in MPEG-4 codec. Therefore, the detection of double compression in MPEG-4 videos as a first step in video forensics research is of significance. In this paper, Markov based features are adopted to detect double compression artifacts, which imply that the original video may have been interpolated. The advantages and limitations of double MPEG-4 compression detection are analyzed. Experimental results have demonstrated that our scheme outperforms most existing methods.

In this paper, an improved video tampering detection model based on MPEG double compression is proposed. Double compression will import disturbance into Discrete Cosine Transform (DCT) coefficients, reflecting in the violation of the parametric logarithmic law for first digit distribution of quantized Alternating Current (AC) coefficients. A 12-D feature can be extracted from each group of pictures (GOP) and machine learning framework is adopted to enhance the detection accuracy. Furthermore, a novel approach with a serial Support Vector Machine (SVM) architecture to estimate original bit rate scale in doubly compressed video is proposed. Experiments demonstrate higher accuracy and effectiveness.