International audience

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.

Cheapfakes, also known as manipulated media or deceptive content, refer to digital creations that have been altered or fabricated with the intention to deceive or mislead. These can include photos, videos, audio recordings, or any form of media that has been manipulated in a way that distorts its original meaning or context using non-AI techniques. The "ACM ICMR 2024 Grand Challenge on Detecting Cheapfakes" has brought attention to the issue of identifying out-of-context misuse, which can greatly assist fact-checkers in their work. To address this challenge, this paper introduces a method to create the training dataset (i.e image-caption triplets dataset) and a novel approach for detecting cheapfakes using four main components (i.e., Natural Language Inference, Context Generation, Prompt Engineering and Out-of-context Classification). In the testing process, we achieved a result with an accuracy of 88.9% on the public test from COSMOS dataset, which is 6.9% higher than the COSMOS baseline. These results demonstrate the potential of our method as a reliable tool for detecting cheapfakes and aiding in the fight against misinformation.

Cheapfakes is a new term for fake media that is made without AI, but with simple tools or captions that deceive or mislead. Cheapfakes include photos, videos, audio recordings, or any media that has been changed to distort its original meaning or context. The "ACM ICMR 2024 Grand Challenge on Detecting Cheapfakes" focuses on the challenge of finding out-of-context (OOC) media, which can assist fact-checkers in their work. By finding OOC media, we can narrow down the search space and raise the chances of discovering cheapfakes. To cope with this challenge, in this research paper, our team propose a novel approach that combines Context Generation and Text Classification as the key components in detecting cheapfakes. Our method was evaluated using 80% of the public test set from the COSMOS dataset, which includes two context labels: out-of-context and not-out-of-context. The results demonstrate an accuracy of 85.9%, validating the effectiveness and reliability of our method in identifying cheapfakes.