With the rapid advancement of text-to-image (T2I) diffusion models, personalized generation has emerged as a prominent research focus. However, existing methods face significant challenges when attempting to disentangle multiple concepts from a single image, such as complex data preparation and severe overfitting. To address these issues, we propose ConceptCraft, an attention-based multi-concept disentanglement method. ConceptCraft leverages self-attention and cross-attention maps to automatically generate precise masks in a single initialization step, without relying on manual annotations or external segmentation models, thereby substantially reducing training overhead. These masks guide the activation of cross-attention during training to effectively separate multiple concepts, while a classspecific attention regularization technique is employed to mitigate overfitting. In addition, we introduce uniform sampling and re-weighted sampling strategies to further enhance generation quality. Experimental results demonstrate that ConceptCraft outperforms existing baselines in both image alignment and text alignment tasks, and exhibits superior robustness in handling complex scenarios. This work presents an efficient and scalable solution for multi-subject personalized generation from a single image.
With the development of the Internet of Things and intelligent sensing technologies, open-source Inertial Measurement Unit (IMU) datasets and open-source models trained on them have become core foundations for research and development in academia and industry. However, their copyright protection faces major challenges: open-source sharing convenience raises data and model theft risks, while methods like encryption and differential privacy clash with open-source principles, and traditional watermarking fails to fit open-source data reuse and model distribution needs. To address the copyright confirmation dilemma of open source IMU datasets and models, this study proposes IMUWatermark, the first frequency-domain backdoor watermarking framework for IMU systems, specifically designed for open source scenarios. It supports watermarking for both open source datasets and open source models, enabling fulllifecycle protection from data sharing to model distribution. Identifying challenges in IMU frequency domain watermarking, such as balancing stealth and effectiveness, adapting to dynamic scenarios, and resisting filter interference, IMUWatermark addresses them through mechanisms such as low-frequency embedding and dynamic parameter adjustment. Experiments on public datasets such as PAMAP2 and MHEALTH verify its effectiveness: Dataset watermarking achieves an attack success rate greater than 90 %, and model watermarking reaches an attack success rate greater than 85 %.
With the rapid advancement of generative forgery technologies, the detection of multi-modal deepfake audio-video content has become an urgent demand in cyber security and forensic analysis. However, detecting audio-video deepfakes remains challenging: forgery traces are often subtle, dispersed, and highly resolution-dependent; existing multimodal methods rely on simple concatenation or shallow interactions, leading to insufficient exploitation of cross-modal consistency. To address these issues, we propose a Cross-level Multi-modal Fusion (CLMF) framework that progressively integrates audio cues into visual representations through cross-level attention, adaptively enhancing complementary information while suppressing redundancy. In addition, we design an Adaptive Audio Feature Enhancement module (AAFE) to highlight subtle frequencydomain artifacts often masked by noise, and a Multi-scale Visual Feature Enhancement module (MVFE) to capture both local texture inconsistencies and global structural distortions. These components jointly achieve robust and consistent cross-modal alignment of forgery traces, leading to significant improvements in deepfake detection performance. On the FakeAVCeleb benchmark, AURORA achieves an accuracy (ACC) of 94.32% and an area under the curve (AUC) of 93.66%, demonstrating superior performance.
Aerospace simulation is a research area attracting increasing attention, as it contributes greatly to shortening experimental time, reducing risks during flight trials, and enhancing spacecraft performance.Recently, computing technologies have been widely adopted in analyzing, verifying, and optimizing spacecraft operational status. However, creating accurate and reliable aerospace simulation models still faces many challenges, such as logical oversimplification and external disruptions. To tackle these challenges, we propose to refine the parameters and assess the credibility of these simulation models. In particular, we first reveal the limited generalization capabilities of existing parameter refinement methods and propose a generalizable deep reinforcement learning approach for parameter optimization. This method exploits reinforcement learning's unique advantages to maximize the expected benefits, thereby enabling effective and accurate parameter refinement in aerospace simulation models. Second, to mitigate the high expenses incurred by multiple simulation experiments, we propose to create a high-precision agent model. We construct a Back-Propagation (BP) neural network with an improved discrete particle swarm algorithm, which enhances the efficiency of simulation experiments. Finally, in order to quantitatively and accurately assess the simulation model's reliability, we propose a novel evaluation method based on Hilbert-Huang algorithm. Our proposed methods have been evaluated in multiple key aerospace scenarios, such as primary and secondary rocket separation and grid rudder applications. The efficiency and effectiveness have been verified by these intensive experiments.
3D object detection is a critical problem in the field of computer vision, widely applied in autonomous driving, robotic navigation, and other domains. Although modern detectors have achieved success in singlesensor object detection, they remain vulnerable to complex environments due to the limitations of single-sensor modalities. We propose SE-D3FNet, a multi-modal fusion framework for 3D object detection that integrates Squeeze-and-Excitation (SE) channel attention and a dynamic 3D focal loss to significantly improve detection accuracy. We present an enhanced feature extraction network termed SE-ResBlock, which demonstrates superior capability in capturing global contextual information. The loss function is also optimized to more accurately capture targets with poor recognition rates. Experimental results on the KITTI benchmark demonstrate that our proposed 3D object detection algorithm achieves superior performance for the car category compared to existing methods.
