The pervasive spread of multimedia misinformation and disinformation presents a significant challenge to information integrity, demanding robust and efficient verification methodologies. Accurately assessing the authenticity and context of complex multimedia content across diverse platforms requires advanced analytical capabilities. This work introduces Ægis, a novel AI-enhanced solution developed as the authors' submission to the ACMMM'25 - Grand Challenge on Multimedia Verification, aimed at improving the efficiency of multimedia verification. The proposed solution integrates both state-of-the-art and traditional verification tools to comprehensively address various aspects of verification tasks, including event summarization, forensic analysis, and evidence validation. In addition to selectively applied verification modules, large language models (LLMs) are leveraged extensively to fuse findings, perform reasoning, and generate structured verification reports-significantly streamlining the verification process. Demonstrated on the competition multimedia dataset, Ægis accurately validates content integrity, extracts geospatial and temporal information, and identifies content origin across platforms, offering reliable and ethical support for real-world fact-checking.

In recent years, video content has become increasingly popular due to the development of technologies, especially for recording devices. This explosion has driven the need for an advanced video-moment retrieval system that can accurately search for specific video segments matching the intentions of the user's queries. A prominent challenge in this field lies in the multimedia nature of the video data, which includes visual, auditory and even textual information. The accuracy and relevance of search results depend on how efficiently a retrieval system processes these types of metadata. In this paper, we introduce IMSearch, an interactive multimedia search system that can retrieve precise video-moment content by executing queries across various information types, such as text, audio, location of the objects, sketch, and human pose. Additionally, we implement a re-ranking mechanism based on user feedback to simultaneously improve and optimize the performance of our IMSearch system. We also explore two different vector libraries (FAISS and HNSW) for vector searching and report the retrieval accuracy and executed time to demonstrate our proposed paradigm.