Item Discrimination (ID) theory, which is widely used in educational assessment, measures the ability of individual test items to differentiate between high and low performers. Inspired by this theory, wepropose anID-induced prompt synthesis frameworkforevaluating LLMs to ensure the evaluation set can continually update and refine according to model abilities.

While recent research suggests that current large Vision-Language Models (VLMs) exhibit more reliance on shape, we find them to still be seriously limited in this regard.

While existing defenses often rely on regularization techniques to reduce information leakage, they remain vulnerable to recent attacks.

This leads to the proposal of a novel self-supervised solution based on a masked loss function. We evaluate our method using both simulated and real data. On simulated data from a conventional video, we achieve 34.35 mean PSNR with extremely photon-sparse binary input (<0.06 photons per pixel per frame).