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.

With the rapid growth of sensors, communication technologies, and social networks, it is now easy to collect large amounts of data from people and their surrounding environments, such as wearable devices, lifelog cameras, and ambient sensors. These data provide both personal and external views of human activities, but most existing work still focuses on analyzing each type of data separately. As a result, there is a clear gap in understanding how to combine and use cross-data across different sources. This workshop provides a platform for researchers from academia and industry to explore cross-data analysis and retrieval, with a focus on practical challenges such as integrating different data types, handling distributed data, and ensuring data security, aiming to support the development of a smart and sustainable society.

Sharing multimodal datasets while protecting privacy remains a challenge in research on social interaction and human behavior. Traditional anonymization often removes useful visual cues, while many generative methods treat each frame independently, leading to inconsistent identities across time and camera views. In this demo, we present an anonymization framework that maintains a stable synthetic identity for each individual throughout a video. By linking observations of people over time and reusing the same generated appearance when they reappear, the system preserves continuity across occlusions and viewpoint changes. This helps retain important contextual signals such as pose, movement, and interactions, while still protecting personal identity. Code repository and video examples available at: https://github.com/Leidiland/TrackAnon.

The ACM Lifelog Search Challenge (LSC) is an annual comparative benchmarking exercise that brings together researchers in the field of multimedia retrieval to evaluate interactive search systems using a large-scale multimodal lifelog dataset. This paper presents an overview of the ninth edition of the challenge (LSC’26), held as a workshop during the ACM International Conference on Multimedia Retrieval (ICMR ’26) in Amsterdam. To broaden its scope, the workshop now features three submission tracks: the traditional Challenge Track for real-time search performance, a new General Lifelog Research Track for theoretical and architectural advancements, and an additional Open Source Track aimed at enhancing reproducibility and reducing barriers to entry for new participants.

For the eighth time since 2018, the ACM Lifelog Search Challenge (LSC) was run to compare interactive lifelog search systems in a live metrics-based challenge. The goal of the LSC workshop is to comparatively evaluate the capabilities of systems accessing a large multimodal lifelog. LSC'25 attracted eleven participating teams, each of which had developed an innovative interactive lifelog retrieval system. The benchmark was organised in a hybrid manner (due to political issues in 2025) at the LSC workshop at ACM ICMR'25 in Chicago, USA. This short paper summarises the LSC workshop setting and presents the participating lifelog search systems.

The sixth edition of the Intelligent Cross-Data Analysis and Retrieval (ICDAR) workshop continues to serve as a forum for researchers and practitioners addressing the integration, analysis, and retrieval of heterogeneous data sources. While individual modalities such as wearable sensors, lifelogging cameras, and social media have been well studied, analyzing cross-data that incorporates multiple perspectives remains a crucial yet challenging task for advancing human-centered applications. In 2025, the workshop received 19 submissions, of which 7 were accepted following a careful peer-review process, resulting in an acceptance rate of 37%. The accepted papers covered a wide range of topics, including zero-shot composed image retrieval, vision-language scene understanding, adaptive modality fusion, lightweight fine-tuning with truncated SVD, and real-world federated split learning on mobile devices. By fostering interdisciplinary collaboration across domains such as well-being, disaster mitigation, mobility, food computing, and smart cities, the workshop continues to highlight emerging challenges and solutions for building intelligent, sustainable, and human-centric systems driven by cross-modal and multimodal data analytics.

Abstract This paper investigates the use of multimedia verification, in particular, computational tools and Open‐source Intelligence (OSINT) methods, for verifying online multimedia content in the context of the ongoing wars in Ukraine and Gaza. Our study examines the workflows and tools used by several fact‐checkers and journalists working at Faktisk, a Norwegian fact‐checking organization. Our study showcases the effectiveness of diverse resources, including AI tools, geolocation tools, internet archives, and social media monitoring platforms, in enabling journalists and fact‐checkers to efficiently process and corroborate evidence, ensuring the dissemination of accurate information. This research provides an in‐depth analysis of the role of computational tools and OSINT methods for multimedia verification. It also underscores the potentials of currently available technology, and highlights its limitations while providing guidance for future development of digital multimedia verification tools and frameworks.

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 explosive growth of social networks this decade has significantly impacted society, with digital technologies enabling anyone to create and disseminate fake news using simple editing tools. Media professionals and fact-checkers are now tasked with the complex challenge of verifying visual content, as both multimedia and associated text can be fabricated. Fact-checkers must thoroughly search for related information, such as the time and place of an image's capture, to uncover inconsistencies for content verification. This process demands expertise in journalism and image analysis and involves navigating vast information spaces with current computational and search tools. While AI can tackle specific tasks, it cannot yet replicate human reasoning and judgment. Our study aims to develop a comprehensive pipeline that employs computer science techniques to assist fact-checkers in working more efficiently and effectively, integrating advanced search tools, deep learning models, and additional data like weather conditions to streamline the verification process.

Our workshop aims to provide a platform for both academic and industrial professionals engaged in the analysis and retrieval of cross-data from diverse perspectives, with a particular emphasis on wearable and ambient sensors, lifelog cameras, social networks, and surrounding sensors.Despite numerous studies exploring individual viewpoints, there remains a significant gap in the analysis and retrieval of cross-data to maximize benefits for humanity.Additionally, challenges such as data security and distributed learning for cross-modal model training and inference arise when dealing with large and distributed datasets.We invite researchers to contribute to this initiative, with the overarching goal of fostering the development of a smart and sustainable society through the efficient utilization of intelligent cross-data analysis and retrieval techniques.

For the seventh time since 2018, the Lifelog Search Challenge (LSC) benchmarked interactive lifelog search systems in a live challenge. The LSC goal is to comparatively evaluate system capabilities to access large multimodal lifelogs comprising hundreds of thousands of records. LSC'24 attracted an unprecedented record number of twenty-one participating teams, where each team proposes innovative ideas implemented to new or already established interactive lifelog retrieval systems. The benchmark was organised in front of a live audience at the LSC workshop at ACM ICMR'24 in Phuket, Thailand. This short paper summarises the LSC workshop setting and presents the participating lifelog search systems.

The recent advancements in Generative Adversarial Networks (GANs) and the emergence of Diffusion models have significantly streamlined the production of highly realistic and widely accessible synthetic content. As a result, there is a pressing need for effective general purpose detection mechanisms to mitigate the potential risks posed by deepfakes. In this paper, we explore the effectiveness of pre-trained vision-language models (VLMs) when paired with recent adaptation methods for universal deepfake detection. Following previous studies in this domain, we employ only a single dataset (ProGAN) in order to adapt CLIP for deepfake detection. However, in contrast to prior research, which rely solely on the visual part of CLIP while ignoring its textual component, our analysis reveals that retaining the text part is crucial. Consequently, the simple and lightweight Prompt Tuning based adaptation strategy that we employ outperforms the previous SOTA approach by 5.01% mAP and 6.61% accuracy while utilizing less than one third of the training data (200k images as compared to 720k). To assess the real-world applicability of our proposed models, we conduct a comprehensive evaluation across various scenarios. This involves rigorous testing on images sourced from 21 distinct datasets, including those generated by GANs-based, diffusion-based and commercial tools. Code and pre-trained models: https://github.com/sohailahmedkhan/CLIPping-the-Deception

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.

The advent of large language models (LLMs) has energised research in Question-Answering (QA) tasks, enabling responses across varied domains like economics and mathematics. Despite their capabilities, LLMs often lack explainability due to their complex parameter embeddings. Additionally, integrating multimedia data into QA systems introduces challenges in processing and interpreting diverse data types such as text, images, audio, and video. This necessitates sophisticated algorithms for accurate information retrieval across media while ensuring the reliability of the data and responses remains a significant challenge. The AIQAM workshop aims to bring together researchers and practitioners to address these challenges and enhance QA systems with multimedia data. The focus is on promoting innovations that improve the accuracy, explainability, and trustworthiness of QA systems, contributing to the development of the field.

In our modern world, the manipulation of digital content, especially the usage of out-of-context images known as Cheapfakes, has become a significant challenge for the endorsement of integrity and trustworthiness for information on the Internet. This highlights an urgent need for effective detection of the misuse of images and their accompanied captions. Motivated by this issue, our research presents an innovative approach to the solution of this problem. Participating in the ACM ICMR 2024 Grand Challenge on Detecting Cheapfakes, we leverage a unified end-to-end network, integrated with generative synthetic data for training. After complete evaluation, our proposed network demonstrated a remarkable accuracy of 95.60% on the public test dataset for Task 1, as well as efficiency in Task 2. This paper highlights the notable potential of employing an end-to-end network for Cheapfakes detection, which composes a significant contribution to the advancement of multimedia content integrity. Our source code is publicly available at https://github.com/thanhson28/cheapfakes_detection_icmr2024.git

For the sixth time since 2018, the Lifelog Search Challenge (LSC) was organized as a comparative benchmarking exercise for various interactive lifelog search systems. The goal of this international competition is to test system capabilities to access large multimodal lifelogs. LSC’23 attracted twelve participanting teams, each of whom had developed a competitive interactive lifelog retrieval system. The benchmark was organized in front of live audience at the LSC workshop at ACM ICMR’23. As in previous editions, this introductory paper presents the LSC workshop and introduces the participating lifelog search systems.

Recently, there has been an increased interest in cross-data research problems, such as predicting air quality using life logging images, predicting congestion using weather and tweets data, and predicting sleep quality using daily exercises and meals. Although several research focusing on multimodal data analytics have been performed, few studies have been conducted on cross-data research (e.g., cross-modal data, cross-domain, cross-platform). The article collection “Intelligent Cross-Data Analysis and Retrieval” aims to encourage research in intelligent cross-data analytics and retrieval and contribute to the creation of a sustainable society. Researchers from diverse domains such as well-being, disaster prevention and mitigation, mobility, climate, tourism and healthcare are welcome to contribute to this Research Topic.

Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust.

We have witnessed the rise of cross-data against multimodal data problems recently. The cross-modal retrieval system uses a textual query to look for images; the air quality index can be predicted using lifelogging images; the congestion can be predicted using weather and tweets data; daily exercises and meals can help to predict the sleeping quality are some examples of this research direction. Although vast investigations focusing on multimodal data analytics have been developed, few cross-data (e.g., cross-modal data, cross-domain, cross-platform) research has been carried on. In order to promote intelligent cross-data analytics and retrieval research and to bring a smart, sustainable society to human beings, the specific article collection on "Intelligent Cross-Data Analysis and Retrieval" is introduced. This Research Topic welcomes those who come from diverse research domains and disciplines such as well-being, disaster prevention and mitigation, mobility, climate change, tourism, healthcare, and food computing

For the fifth time since 2018, the Lifelog Search Challenge (LSC) facilitated a benchmarking exercise to compare interactive search systems designed for multimodal lifelogs. LSC'22 attracted nine participating research groups who developed interactive lifelog retrieval systems enabling fast and effective access to lifelogs. The systems competed in front of a hybrid audience at the LSC workshop at ACM ICMR'22. This paper presents an introduction to the LSC workshop, the new (larger) dataset used in the competition, and introduces the participating lifelog search systems.

Cross-data analytics and retrieval have gained significant improvement recently. People can now extract more data insights precisely and quickly towards having many excellent applications serving human lives. Since people create multimedia and other types of data that reflect the diverse perspectives of human lives, these data are just pieces of the puzzle of the world's pictures. Hence, it is necessary to assembly all these pieces towards having a better solution for human-centered problems. Hence, the workshop welcomes those who work with multimedia and others and come from diverse research domains and disciplines to work on intelligent cross-data analytics and retrieval to bring a smart, sustainable society to human beings. The research domain can vary from well-being, disaster prevention and mitigation, mobility to food computing, to name a few.

The Lifelog Search Challenge (LSC) is an annual benchmarking challenge for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'21, the fourth challenge, attracted sixteen participants, each of which had developed interactive retrieval systems for large multimodal lifelogs. These interactive retrieval systems participated in a comparative evaluation in front of an online live-audience at the LSC workshop at ACM ICMR'21. This overview presents the motivation for LSC'21, the lifelog dataset used in the competition, and the participating systems.

Nowadays, recommendation systems play an indispensable role in many fields, including e-commerce, finance, economy, and gaming. There is emerging research on publication venue recommendation systems to support researchers when submitting their scientific work. Several publishers such as IEEE, Springer, and Elsevier have implemented their submission recommendation systems only to help researchers choose appropriate conferences or journals for submission. In this work, we present a demo framework to construct an effective recommendation system for paper submission. With the input data (the title, the abstract, and the list of possible keywords) of a given manuscript, the system recommends the list of top relevant journals or conferences to authors. By using state-of-the-art techniques in natural language understanding, we combine the features extracted with other useful handcrafted features. We utilize deep learning models to build an efficient recommendation engine for the proposed system. Finally, we present the User Interface (UI) and the architecture of our paper submission recommendation system for later usage by researchers.

The Lifelog Search Challenge (LSC) is an annual comparative benchmarking activity for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'20, the third such challenge, attracts fourteen participants with their interactive lifelog retrieval systems. These systems are comparatively evaluated in front of a live-audience at the LSC workshop at ACM ICMR'20 in Dublin, Ireland. This overview motivates the challenge, presents the dataset and system configuration used in the challenge, and briefly presents the participating teams.

With the rapid growth of users' data in SaaS (Software-as-a-service) platforms using micro-services, it becomes essential to detect duplicated entities for ensuring the integrity and consistency of data in many companies and businesses (primarily multinational corporations). Due to the large volume of databases today, the expected duplicate detection algorithms need to be not only accurate but also practical, which means that it can release the detection results as fast as possible for a given request. Among existing algorithms for the deduplicate detection problem, using Siamese neural networks with the triplet loss has become one of the robust ways to measure the similarity of two entities (texts, paragraphs, or documents) for identifying all possible duplicated items. In this paper, we first propose a practical framework for building a duplicate detection system in a SaaS platform. Second, we present a new active learning schema for training and updating duplicate detection algorithms. In this schema, we not only allow the crowd to provide more annotated data for enhancing the chosen learning model but also use the Siamese neural networks as well as the triplet loss to construct an efficient model for the problem. Finally, we design a user interface of our proposed deduplicate detection system, which can easily apply for empirical applications in different companies.

Nowadays, almost everyone holds some form or other of a personal life archive. Automatically maintaining such an archive is an activity that is becoming increasingly common, however without automatic support the users will quickly be overwhelmed by the volume of data and will miss out on the potential benefits that lifelogs provide. In this paper we give an overview of the current status of lifelog research and propose a concept for exploring these archives. We motivate the need for new methodologies for indexing data, organizing content and supporting information access. Finally we will describe challenges to be addressed and give an overview of initial steps that have to be taken, to address the challenges of organising and searching personal life archives.

Being able to automatically link social media information and data to remote-sensed data holds large possibilities for society and research. In this paper, we present a system called JORD that is able to autonomously collect social media data about technological and environmental disasters, and link it automatically to remote-sensed data. In addition, we demonstrate that queries in local languages that are relevant to the exact position of natural disasters retrieve more accurate information about a disaster event. To show the capabilities of the system, we present some examples of disaster events detected by the system. To evaluate the quality of the provided information and usefulness of JORD from the potential users point of view we include a crowdsourced user study.