Cross-age face retrieval—often formulated as searching a large-scale gallery for the same identity across significant age gaps—remains a fundamental challenge due to the geometric mismatch between static embedding spaces and dynamic biological aging. In real-world retrieval and forensic deployments, preserving the pre-trained identity metric is crucial, yet many existing approaches either synthesize pixels with artifacts or fine-tune backbones, potentially distorting the cosine-based embedding geometry. In this paper, we propose Spherical Projected Bézier Flow (SPBF), a geometry-constrained manifold transport framework that models aging as feature transport on the hypersphere while keeping the backbone frozen. SPBF parameterizes a curvilinear trajectory via a projected Bézier path and learns a tangent velocity field with an Endpoint Consistency Constraint, enabling flexible non-linear and variable-speed dynamics without leaving the unit sphere. By avoiding off-manifold, low-norm states associated with higher uncertainty, SPBF improves retrieval robustness under large age gaps. Extensive experiments on FG-NET, AgeDB, and CACD show that SPBF is competitive with SOTA on homogeneous benchmarks and delivers substantial gains in cross-dataset generalization with only a lightweight plug-in module.

The rapid advancement of generative models has led to the widespread emergence of highly realistic synthetic images, making the detection of AI-generated content increasingly critical. In particular, diffusion models have recently achieved unprecedented levels of visual fidelity, further raising concerns. While most existing approaches rely on supervised learning, zero-shot detection methods have attracted growing interest due to their ability to bypass data collection and maintenance. Nevertheless, the performance of current zero-shot methods remains limited. In this paper, we introduce a novel zero-shot AI-generated image detection method. Unlike previous works that primarily focus on identifying artifacts in the final generated images, our work explores features within the image generation process that can be leveraged for detection. Specifically, we simulate the image sampling process via diffusion-based inversion and observe that the denoising outputs of generated images converge to the target image more rapidly than those of real images. Inspired by this observation, we compute the similarity between the original image and the outputs along the denoising trajectory, which is then used as an indicator of image authenticity.Since our method requires no training on any generated images, it avoids overfitting to specific generative models or dataset biases. Experiments across a wide range of generators demonstrate that our method achieves significant improvements over state-of-the-art supervised and zero-shot counterparts.