TL;DR
The findings demonstrate that AI-generated images serve as an effective diagnostic tool for robustness evaluation and failure mode analysis, and supports reliable deployment of product image quality assessment models and highlights the value of synthetic data as a structured robustness testing resource in multimedia and e-commerce systems.
Patch Knowledge Transfer is proposed, a knowledge distillation-based optimization framework that achieves synergistic optimization of visual representation capability and inference efficiency through an innovative multi-level knowledge transfer mechanism and achieves a superior balance between model efficiency and asse...
A cross-domain AI-generated IQA via content-distortion awareness (CDAQA) is proposed, designed to update the existing IQA model for AGIs and achieves higher accuracy and stability in cross-domain AGIs tasks.
This model leverages different layers of the large language model (LLM) decoder, treating them as multiple detectors that perform synchronous distortion detection using multi-level features, which helps mitigate the ambiguous foreground-background separation commonly encountered in the S2A problem.
A unified and fully reproducible benchmarking framework that standardises preprocessing, enforces fixed train–test splits, aligns training configurations, and evaluates models using a consistent multi-metric suite is introduced, demonstrating that compute-aligned, multi-metric benchmarking is essential for fair and rep...
This work establishes a new paradigm for generated image detection by recasting the detection task as a problem of machine unlearning, and introduces two detection methods: data-free detection, which prunes model parameters to induce unlearning without data access, and data-driven detection, which optimizes LVMs to unl...
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