Abscesses in the head and neck represent an acute infectious process that can potentially lead to sepsis or mortality if not diagnosed and managed promptly. Accurate detection and delineation of these lesions on imaging are essential for diagnosis, treatment planning, and surgical intervention. In this study, we introduce AbscessHeNe, a curated and comprehensively annotated dataset comprising 4,926 contrastenhanced CT slices with clinically confirmed head and neck abscesses. The dataset is designed to facilitate the development of robust semantic segmentation models that can accurately delineate abscess boundaries and evaluate deep neck space involvement, thereby supporting informed clinical decision-making. To establish performance baselines, we evaluate several state-of-the-art segmentation architectures, including CNN, Transformer, and Mamba-based models. The highestperforming model achieved a Dice Similarity Coefficient of 0.39, Intersection-over-Union of 0.27, and Normalized Surface Distance of 0.67, indicating the challenges of this task and the need for further research. Beyond segmentation, AbscessHeNe is structured for future applications in content-based multimedia indexing and case-based retrieval. Each CT scan is linked with pixel-level annotations and clinical metadata, providing a foundation for building intelligent retrieval systems and supporting knowledge-driven clinical workflows. The dataset will be made publicly available at https://github.com/drthaodao3101/AbscessHeNe.git.

The increasing sophistication of cheapfakes and deepfakes poses new challenges for maintaining information integrity in the digital age. While recent advances in deep learning classifiers and chatbot-assisted verification have greatly improved detection capabilities, there remains an opportunity to further strengthen these systems by incorporating contextual understanding from original source material. In this paper, we propose a multimedia verification framework that complements existing approaches by retrieving and leveraging contextual evidence associated with the target media to support more informed assessments of authenticity. For cheapfake detection, we analyze the relationship between image-caption pairs to determine if the caption is out-of-context (OOC). Using the COSMOS dataset, our model achieves a state-of-the-art accuracy of 93.6%. For deepfake detection, we segment videos by speaker using diarization, extract key frames, and apply the GenConViT model for face analysis. We further retrieve related news articles and contextual metadata (speaker identity, topic, time) to support chatbot reasoning. On the DF-W YouTube dataset, our system achieves an F1 score of 89%, surpassing existing benchmarks. Our results demonstrate the importance of integrating contextual retrieval and reasoning into multimedia verification pipelines, especially when verifying AI-generated or manipulated content.

The Event-Enriched Image Analysis (EVENTA) Grand Challenge, hosted at ACM Multimedia 2025, introduces the first large-scale benchmark for event-level multimodal understanding. Traditional captioning and retrieval tasks largely focus on surface-level recognition of people, objects, and scenes, often overlooking the contextual and semantic dimensions that define real-world events. EVENTA addresses this gap by integrating contextual, temporal, and semantic information to capture the who, when, where, what, and why behind an image. Built upon the OpenEvents V1 dataset, the challenge features two tracks: Event-Enriched Image Retrieval and Captioning, and Event-Based Image Retrieval. A total of 45 teams from six countries participated, with evaluation conducted through Public and Private Test phases to ensure fairness and reproducibility. The top three teams were invited to present their solutions at ACM Multimedia 2025. EVENTA establishes a foundation for context-aware, narrative-driven multimedia AI, with applications in journalism, media analysis, cultural archiving, and accessibility. Further details about the challenge are available at the official homepage: https://ltnghia.github.io/eventa/eventa-2025.