The 2026 Grand Challenge on Multimedia Verification at ICMR 2026 invited participants to verify real-world multimedia content across various dimensions, such as verified evidence (source, location, time, entities, motivation) and forensic analysis. Four teams submitted complete solutions, evaluated by professional fact-checkers on a 110-point rubric across ten real-world cases. This paper presents an overview of the challenge, summarizes the participating methods, and reports the official scoreboard together with per-criterion analysis. We observe a clear trend toward multi-agent, tool-augmented LLM pipelines, but also recurring weaknesses in human oversight, reasoning grounding, and resistance to hallucination. As a result, no submission was declared a winner this year, and we outline concrete directions for the next edition.

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.

Information disorder is one of the most typical challenges in the current era of science and technology. The amount of information on the internet is increasing, but its correctness and authenticity are not always guaranteed, leading to false information, fake news, etc. The mentioned problem negatively affects users' reception and use of information. Unlike deepfake, cheapfake is created using simple techniques and does not rely on AI to produce fake multimedia. Cheapfake is becoming increasingly popular due to its ease of creation. Thus, there is a growing need to develop techniques that can detect cheapfake content. Following previous events, the Grand Challenge on Detecting Cheapfakes at ACM ICMR 2024 continues to seek contributions from researchers on cheapfake detection with the goal of improving effectiveness and creativity in approach, and understanding the limitations of the current dataset. This challenge has accepted 6 new proposed methods from participants with the highest private test accuracies achieved at 72.2% for Task 1 and 54.84% for Task 2. The highest public test accuracies for the two tasks are 95.6% and 93% respectively. These new methods focus on incorporating new AI models such as Stable Diffusion, LLM. These new findings represent the latest advancements in cheapfake detection research and introduce new potential approaches for future research.

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.

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.