The remarkable realism of images generated by diffusion models poses critical detection challenges. Current methods utilize reconstruction error as a discriminative feature, exploiting the observation that real images exhibit higher reconstruction errors when processed through diffusion models. However, these approaches require costly reconstruction computations and depend on specific diffusion models, making their performance highly model-dependent. We identify a fundamental difference: real images are more difficult to fit with Gaussian distributions compared to synthetic ones. In this paper, we propose Forgery Identification via Noise Disturbance (FIND), a novel method that requires only a simple binary classifier. It eliminates reconstruction by directly targeting the core distributional difference between real and synthetic images. Our key operation is to add Gaussian noise to real images during training and label these noisy versions as synthetic. This step allows the classifier to focus on the statistical patterns that distinguish real from synthetic images. We theoretically prove that the noise-augmented real images resemble diffusion-generated images in their ease of Gaussian fitting. Furthermore, simply by adding noise, they still retain visual similarity to the original images, highlighting the most discriminative distribution-related features. The proposed FIND improves performance by 11.7% on the GenImage benchmark while running 126x faster than existing methods. By removing the need for auxiliary diffusion models and reconstruction, it offers a practical, efficient, and generalizable way to detect diffusion-generated content.

Whole slide imaging (WSI) provides rich tissue information at a gigapixel resolution, posing significant challenges for the development of pathology analysis algorithms. Mainstream approaches effectively analyze WSI but treat the pretrained feature extractor and the task-specific network as independent modules, thereby restricting downstream task accuracy due to the limitations of the pretrained model. Inspired by the knowledge complementarity mechanism in multi-agent collaboration, we propose PLM-SynNet, a pathology large model synergy network that integrates the strengths of multiple pathology large models (PLMs) by establishing a flexible collaborative structure and achieving information gain. Specifically, the PLM Synergy Block (PLM-SB) is designed based on Mixture of Experts (MoE), which flexibly generates and utilizes supplementary features by employing a feature generator as an expert in MoE and merging these outputs via pixel-wise summation for effective collaboration. Subsequently, a Synergy Reinforcement Loss (SRLoss) is defined to enhance the information gain of multiple PLMs by enforcing stricter constraints on both queried and generated features. Experiments on a private PCA-EPE (Extraprostatic Extension of Prostate Cancer) dataset and two public datasets demonstrate the effectiveness of the proposed method, yielding gains of 13.27% in F1-score, 6.30% in accuracy, and 15.16% in MCC on PCA-EPE. It further improves TCGA-CRC accuracy by 1.71% and enhances BRIGHT accuracy and AUC by 2.67% and 4.00%, respectively. The code repository is available at https://github.com/mathfyy/PLM-SynNet.

We present MFRNet, a novel network for multi-modal object re-identification that integrates multi-modal data features to effectively retrieve specific objects across different modalities. Current methods suffer from two principal limitations: (1) insufficient interaction between pixel-level semantic features across modalities, and (2) difficulty in balancing modality-shared and modality-specific features within a unified architecture. To address these challenges, our network introduces two core components. First, the Feature Fusion Module (FFM) enables fine-grained pixel-level feature generation and flexible cross-modal interaction. Second, the Feature Representation Module (FRM) efficiently extracts and combines modality-specific and modality-shared features, achieving strong discriminative ability with minimal parameter overhead. Extensive experiments on three challenging public datasets (RGBNT201, RGBNT100, and MSVR310) demonstrate the superiority of our approach in terms of both accuracy and efficiency, with 8.4% mAP and 6.9% accuracy improved in RGBNT201 with negligible additional parameters.

The challenge of inconsistent modalities in real-world applications presents significant obstacles to effective object re-identification (ReID). However, most existing approaches assume modality-matched conditions, significantly limiting their effectiveness in modality-mismatched scenarios. To overcome this limitation and achieve a more flexible ReID, we introduce MDReID to allow any-to-any image-level ReID systems. MDReID is inspired by the widely recognized perspective that modality information comprises both modality-shared features, predictable across modalities, and unpredictable modality-specific features, which are inherently modality-dependent and consist of two key components: the Modality Decoupling Module (MDM) and Modality-aware Metric Learning (MML). Specifically, MDM explicitly decomposes modality features into modality-shared and modality-specific representations, enabling effective retrieval in both modality-aligned and mismatched scenarios. MML, a tailored metric learning strategy, further enhances feature discrimination and decoupling by exploiting distributional relationships between shared and specific modality features. Extensive experiments conducted on three challenging multi-modality ReID benchmarks (RGBNT201, RGBNT100, MSVR310) consistently demonstrate the superiority of MDL. MDReID achieves significant mAP improvements of 9.8\%, 3.0\%, and 11.5\% in modality-matched scenarios, and average gains of 3.4\%, 11.8\%, and 10.9\% in modality-mismatched scenarios, respectively.