The proliferation of online misinformation has underscored the urgent need for robust, comprehensive verification tools. Existing approaches often lack the capacity for multi-modal analysis and evidence-based explainability, limiting their effectiveness in diverse, real-world scenarios. DeepVerify presents a complete end-to-end software solution for evidence-based online information verification, integrating previously published modules from ACMMM 2025 and supporting multi-lingual users. The system introduces a novel algorithm for detecting AI-generated content in images and text, extending prior capabilities that focused solely on video. The core idea of the algorithm is to compare the similarity of the input with information available online, allowing for the detection of AI-generated content. As a result, the system not only determines whether the data is created by humans or machines, but also provides specific evidence to support this decision. Unlike previous approaches that merely output a confidence score, this method offers concrete supporting evidence for its verdict, enhancing transparency and reliability in the verification process. Experiments conducted on benchmark datasets demonstrate that DeepVerify consistently outperforms current state-of-the-art methods, offering enhanced accuracy and transparent reasoning across multiple modalities. This work sets a new standard for explainable, reliable online information verification suitable for global audiences.

Worldwide image geolocalization, which aims to predict the GPS coordinates of any image on Earth, remains challenging due to global visual diversity. Recent generative approaches based on Retrieval-Augmented Generation (RAG) and Large Multimodal Models (LMMs) leverage candidates retrieved from fixed databases for reasoning, but often struggle with scenes that are absent from the reference set. In this work, we propose GeoSearch, an open-world geolocation framework that integrates web-scale reverse image search into the RAG pipeline. GeoSearch augments LMM prompts with database-retrieved coordinates and textual evidence extracted from web pages. To mitigate noise from irrelevant content, we introduce a two-layer filtering mechanism consisting of image matching, followed by confidence-based gating. Experiments on standard benchmarks Im2GPS3k and YFCC4k demonstrate the superiority of GeoSearch under leakage-aware evaluation. Our code and data are publicly available to support reproducibility.

The proliferation of multimedia content on social media has transformed how information is produced and consumed, enabling real-time coverage of global events but also accelerating the spread of misinformation, particularly during crises such as wars, natural disasters, and elections. The rise of synthetic media and the reuse of authentic content in misleading contexts further underscore the need for robust verification tools. In this paper, we present a comprehensive system developed for the ACM Multimedia 2025 Grand Challenge on Multimedia Verification. Our system evaluates both the authenticity and contextual accuracy of multimedia content in multilingual settings, producing expert-oriented verification reports alongside accessible summaries for the public. We propose a unified verification pipeline that integrates visual forensics, textual analysis, and multimodal reasoning, with a hybrid approach to detecting out-of-context (OOC) media via semantic similarity, temporal alignment, and geolocation cues. Extensive evaluations on the challenge benchmark demonstrate the system's effectiveness across diverse real-world scenarios. Our contributions advance the state of the art in multimedia verification while providing practical tools for journalists, fact-checkers, and researchers addressing information integrity in the digital age.