This study systematically uncovers and quantitatively evaluates the pervasive Orientalist biases in text-to-image (T2I) and text-to-video (T2V) generation models through a sociocultural lens grounded in postcolonial Orientalist theoretical frameworks. We identify systematic biases in the visual representations produced by multimodal generative models, including hyper-exoticization and temporal alienation. These biases mirror colonial-era narratives and undermine equitable sociocultural communication. Through empirical analysis of 8 mainstream T2I models and 4 T2V models, we demonstrate that culturally neutral prompts related to China consistently generate visual outputs embedded with Orientalist biases. We develop a novel visual question answering (VQA) framework as an evaluation metric, leveraging state-of-the-art vision-language model (VLM) to establish the first automated quantitative assessment methodology for such biases. A mitigation framework employing large language model (LLM) is proposed and experimentally validated. This interdisciplinary work illuminates the societal implications of multimodal generative models while advancing efforts toward fair and inclusive social computing.

The security landscape of software systems has witnessed considerable advancements through dynamic testing methodologies, especially fuzzing. Traditionally, fuzzing involves a sequential, cyclic process where software is tested to identify crashes. These crashes are then triaged and patched, leading to subsequent cycles that uncover further vulnerabilities. While effective, this method is not efficient as each cycle potentially reveals new issues previously obscured by earlier crashes, thus resulting in vulnerabilities being discovered sequentially.