Human trajectory prediction requires modeling both individual motion patterns and social interactions among agents. Existing methods have made substantial progress by using attention mechanisms, graph structures, and temporal encoders to capture dynamic social context. However, most of them primarily focus on how social information is encoded, while paying less explicit attention to how the encoded social context should take effect during future trajectory generation. In this paper, we argue that dynamic social encoding does not necessarily imply dynamic social activation. The same interaction context may require different activation strengths across future horizons and scene densities: social cues should be strengthened when interaction evidence is strong, but suppressed when they are weak or noisy. To address this issue, we propose GEAR, a generation-aware bias activation model for human trajectory prediction. Built upon a bias-decomposed trajectory generation formulation, GEAR dynamically activates the individual-motion and social-resonance bias terms at each future step before final trajectory composition. This allows the model to explicitly control when and how strongly individual and social bias components participate in generation. Experiments on ETH-UCY, SDD, and NBA show that GEAR consistently improves the resonance-based baseline and achieves competitive state-of-the-art performance. Further analyses of activation patterns and density-grouped errors validate the importance of calibrating encoded social context during trajectory generation. Our code is available at https://github.com/11isnotavailable/GEAR.git.
Jia-Heng Chen, Jia-Xing Li, Leixia Wang et al.· 0 citations
Diffusion models have become the dominant approach for text-to-image generation, but their ability to memorize training data raises increasing concerns about privacy leakage. Differential privacy (DP) is widely adopted to mitigate such privacy risks during model fine-tuning, yet the practical privacy leakage of differentially private diffusion models remains difficult to assess, especially in black-box settings where only generated images are observable. Existing auditing methods for diffusion models typically rely on membership inference attacks based on whole-image similarity between generated samples and target images. However, such approaches may underestimate privacy leakage when similarity between generated images and training samples is concentrated in localized image regions rather than at the whole-image level. In this work, we explore privacy leakage in diffusion models fine-tuned with differential privacy from a black-box perspective. We propose an empirical privacy assessment framework that leverages local image information, instead of treating images as indivisible wholes, to improve the distinguishability of privacy leakage signals. To improve privacy auditing efficiency and reduce sampling variance, we further leverage the image inpainting interface of diffusion models to perform region-focused auditing in a fully black-box setting. Extensive experiments across different fine-tuning and auditing settings demonstrate that our approach provides more reliable empirical assessments of privacy leakage than whole-image-based auditing methods.
Jingnan Xu, Leixia Wang, Xiaofeng Meng· Proceedings of the 32nd ACM...· 0 citations
Diffusion models have become the dominant approach for text-to-image generation, but their ability to memorize training data raises increasing concerns about privacy leakage. Differential privacy (DP) is widely adopted to mitigate such privacy risks during model fine-tuning, yet the practical privacy leakage of differentially private diffusion models remains difficult to assess, especially in black-box settings where only generated images are observable. Existing auditing methods for diffusion models typically rely on membership inference attacks based on whole-image similarity between generated samples and target images. However, such approaches may underestimate privacy leakage when similarity between generated images and training samples is concentrated in localized image regions rather than at the whole-image level. In this work, we explore privacy leakage in diffusion models fine-tuned with differential privacy from a black-box perspective. We propose an empirical privacy assessment framework that leverages local image information, instead of treating images as indivisible wholes, to improve the distinguishability of privacy leakage signals. To improve privacy auditing efficiency and reduce sampling variance, we further leverage the image inpainting interface of diffusion models to perform region-focused auditing in a fully black-box setting. Extensive experiments across different fine-tuning and auditing settings demonstrate that our approach provides more reliable empirical assessments of privacy leakage than whole-image-based auditing methods.
Jingnan Xu, Leixia Wang, Xiaofeng Meng· Proceedings of the 32nd ACM...· 0 citations
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