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.

The rise of social media enables access to valuable information but also fuels the spread of fake news and misinformation. Cheapfake is a type of misinformation created through simple techniques, often involving the use of unaltered images with misleading captions. To distinguish between Out-of-Context (OOC) and Not-Out-of-Context (NOOC) image-caption pairs, prior research has used text-to-image generative models to generate images from captions and then extract correlations between the generated images and the original images. Despite being unable to identify contradictions in the caption pairs, the aforementioned work has demonstrated promising potential for using generative models in cheapfake detection. In this paper, we introduce a novel framework that leverages a generative model to join the contents of an original image and a caption into a new image, referred to as a context-synthetic image. To convert the quantitative difference between an original image and a context-synthetic image, termed as the contextual deviation value, into OOC and NOOC labels, we train a classification model on a newly curated dataset of 7144 context-synthetic images generated using the Stable Diffusion model. We believe that our work offers valuable insights into the use of generative models for cheapfake detection, paving the way for future advancements in this field.