arXiv subjects

Jingrui Xu

Publications and source records attributed to Jingrui Xu.

3 recordsLinked to original sources

arXiv2026

GlobalForge: Towards Robust AI-Generated Image Detection

AI-generated image (AIGI) detectors achieve strong accuracy on clean benchmarks, but their performance drops sharply after images are propagated through real-world channels. We trace this fragility to what these detectors actually learn: they overfit to local artifacts left by generators in small spatial neighborhoods, which are easily destroyed by common propagation degradations such as JPEG compression and blur. Instead, we shift the discriminative cue from fragile local artifacts to more robust global structure. Building on this, we propose GlobalForge, a framework with two complementary modules. The Local Information Bottleneck (LIB) suppresses local components to block shortcut learning, while the Global Structural Reasoning (GSR) module forces every token to gather evidence from distant regions. Both modules are trained jointly under a contrastive structural loss based on degradation that keeps the resulting features stable under degradation. To support fine-grained robustness evaluation, we further introduce RealDeg-Bench, covering 7 common degradation operators and multi-step compound chains. GlobalForge improves average BAcc on 8 in-the-wild benchmark groups by $\mathbf{5.89\%}$ over the previous state-of-the-art, and is clearly ahead of representative baselines on RealDeg-Bench under both single and compound degradations. Code is available at https://anonymous.4open.science/r/GlobalForge-BE0F/.

cs.CV↗

arXiv2026

Rethinking the Readout: Unlocking Video Backbones for AI-Generated Video Detection

AI-generated videos (AIGVs) typically contain subtle temporal artifacts that arise from inter-frame inconsistencies rather than within individual frames. A detector that captures such artifacts should therefore benefit from video pretrained backbones over image only ones. In practice, however, video backbones with standard global readouts often fail to outperform strong image pretrained probes on AIGV benchmarks. We attribute this gap to excessive spatiotemporal aggregation in the readout. Video pretrained backbones tend to compress each frame into a single global descriptor. This compression suppresses local patch level temporal dynamics and discards inter patch relations, which are precisely the cues that AIGV detection most reliably depends on. Based on this, we propose Velocity Gated Patch Velocity Profiling (V-PVP), a lightweight readout that replaces only the aggregation layer with two parallel streams over the patch velocity field, adding only about $0.5$M trainable parameters. V-PVP serves as a general plug-and-play module that consistently improves performance across diverse video backbones under both end-to-end fine-tuning and linear probing settings. Our method reaches \textbf{95.28} AUC on AIGVDBench while keeping the backbone fully frozen. The results show that simply replacing the aggregation layer reactivates the temporal potential of frozen video backbones, restoring their advantage on AIGV detection. Code is available at https://anonymous.4open.science/r/PVP-81B3/.

cs.CV↗

arXiv2026

How Do Ice Shelves Calve? Peridynamic Modeling of Ice Shelf Fracture Driven by Wave Erosion, Basal Melting, and Buoyancy Flexure

An ice shelf is a floating extension of a land-based ice sheet into the ocean. It plays a crucial role in slowing down the flow of land ice into the sea, thus stabilizing the ice sheet. However, this stabilizing effect can be weakened by ice calving, a process in which large fragments of ice detach from the ice shelf. Although ice calving is widely acknowledged as a major contributor to ice mass loss, and its frequency and magnitude are highly sensitive to the environmental forcing, the underlying physics-based mechanisms remain poorly understood, particularly under ocean wave actions. In this context, we developed a nonlocal peridynamics (PD) framework to model the ice calving process subjected to wave-induced frontal corrosion. The proposed physics-based PD framework enables investigation of the coupled effects of self-weight bending, buoyancy-induced foot loosening, and ice calving process. To authors' best knowledge, this work represents the first attempt to employ a physics-based peridynamics framework for simulating ice calving processes. Compared with conventional finite element methods (FEM), the PD framework naturally captures crack initiation, interaction, and propagation without the need for special numerical treatments, thereby providing a robust tool for simulating fracture phenomena under large deformations and long-term environmental loading. To quantitatively resolve fracture processes, we implemented a static first Piola Kirchhoff virial stress formulation within the PD framework, allowing direct evaluation of stress concentration and energy release at evolving crack tips. Subsequently, the model is rigorously validated through one-to-one comparisons with finite-element stress fields, analytical beam-theory solutions, and recent field observations of wave-driven ice-shelf failure reported by Sartore et al. (2025).

cs.CE↗