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Jin Song

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4 papers

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AAAI Conference 2026 Conference Paper

Hashed Watermark as a Filter: A Unified Defense Against Forging and Overwriting Attacks in Neural Network Watermarking

Yuan Yao
Jin Song
Jian Jin

As valuable digital assets, deep neural networks necessitate robust ownership protection, positioning neural network watermarking (NNW) as a promising solution. Among various NNW approaches, weight-based methods are favored for their simplicity and practicality; however, they remain generally vulnerable to forging and overwriting attacks. To address those challenges, we propose *NeuralMark*, a robust method built around a *hashed watermark filter*. Specifically, we utilize a hash function to generate an irreversible binary watermark from a secret key, which is then used as a filter to select the model parameters for embedding. This design cleverly intertwines the embedding parameters with the hashed watermark, providing a robust defense against both forging and overwriting attacks. Average pooling is also incorporated to resist fine-tuning and pruning attacks. Furthermore, it can be seamlessly integrated into various neural network architectures, ensuring broad applicability. We theoretically analyze its security boundary and highlight the necessity of using a hashed watermark as a filtering mechanism. Empirically, we demonstrate its effectiveness and robustness across 13 distinct Convolutional and Transformer architectures, covering five image classification tasks and one text generation task.

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AAAI Conference 2025 Conference Paper

HomeDiffusion: Zero-Shot Object Customization with Multi-View Representation Learning for Indoor Scenes

Guoqiu Li
Jin Song
Yiyun Fei

Recently, zero-shot object customization generation methods have rapidly developed and shown tremendous potential for applications. For instance, in the e-commerce domain, consumers can observe the visual effect of furniture placed within their personal living spaces or clothes worn on their own bodies. Many existing approaches perform object customization generation based on diffusion models and extracted reference object features. However, the generated object significantly diverges from the original reference object in details such as patterns and curves. Particularly for asymmetrical reference objects, the absence of comprehensive multi-viewpoint information prevents the generation of object poses that harmonize with the background scene. To address these shortcomings, we have constructed a novel dataset comprising multi-angle images of furniture and indoor scenes. Based on diffusion models, we introduce HomeDiffusion, which can leverage multi-viewpoint images of the same reference object to accurately generate visually harmonious object poses within specified areas of the background scene. During the diffusion process, we further extract high-fidelity details of the reference object and perform cross-attention with the noise latents in the latent space, thereby ensuring the preservation of details in the customized object generation. Extensive qualitative and quantitative experiments demonstrate that our method achieves superior performance over other existing zero-shot as well as few-shot object customization approaches.

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NeurIPS Conference 2025 Conference Paper

ProteinConformers: Benchmark Dataset for Simulating Protein Conformational Landscape Diversity and Plausibility

Yihang Zhou
Chen Wei
Minghao Sun
Jin Song
Yang Li
Lin Wang
Yang Zhang

Understanding the conformational landscape of proteins is essential for elucidating protein function and facilitating drug design. However, existing protein conformation benchmarks fail to capture the full energy landscape, limiting their ability to evaluate the diversity and physical plausibility of AI-generated structures. We introduce ProteinConformers, a large-scale benchmark dataset comprising over 381, 000 physically realistic conformations for 87 CASP targets. These were derived from more than 40, 000 structural decoys via extensive all-atom molecular dynamics simulations totaling over 6 million CPU hours. Using this dataset, we propose novel metrics to evaluate conformational diversity and plausibility, and systematically benchmark six protein conformation generative models. Our results highlight that leveraging large-scale protein sequence data can enhance a model’s ability to explore conformational space, potentially reducing reliance on MD-derived data. Additionally, we find that PDB and MD datasets influence model performance differently, current models perform well on inter-atomic distance prediction but struggle with inter-residue orientation generation. Overall, our dataset, evaluation metrics, and benchmarking results provide the first comprehensive foundation for assessing generative models in protein conformational modeling. Dataset and instructions are available at https: //huggingface. co/ datasets/Jim990908/ProteinConformers/tree/main. Codes are stored at https: //github. com/auroua/ProteinConformers. An interactive website locates at https: //zhanggroup. org/ProteinConformers.

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EAAI Journal 2020 Journal Article

Stabilization of stochastic time-varying coupled systems with delays and Lévy noise on networks based on aperiodically intermittent control

Hui Zhou
Jin Song
Wenxue Li

The aim of this paper is to research the stabilization of stochastic time-varying coupled systems with delays and Lévy noise on networks (STSDLN) via aperiodically intermittent control. It is worth pointing out that the time-varying coupling is considered into Lévy noise systems in the first time. Then, by means of a graph-theoretic approach, Lyapunov method and some techniques of inequalities, some stabilization criteria are obtained to guarantee exponentially stability in mean square for STSDLN. Therein, we weaken the sufficient condition for dealing with the time-varying coupling compared to the existing literature, which can reduce the conservation of the conclusions. Additionally, the intensity of control is closely related to the perturbed intensity of noise and the time-varying coupling strength. In particular, as a practical application of our theoretical results, the stabilization of stochastic time-varying coupled oscillators with delays and Lévy noise on networks is studied. Finally, a numerical example is provided to illustrate the validity of the results obtained.

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