Latent Multimodal Reconstruction for Misinformation Detection

• URL: http://arxiv.org/abs/2504.06010v1
• Date: Tue, 08 Apr 2025 13:16:48 GMT
• Title: Latent Multimodal Reconstruction for Misinformation Detection
• Authors: Stefanos-Iordanis Papadopoulos, Christos Koutlis, Symeon Papadopoulos, Panagiotis C. Petrantonakis,
• Abstract summary: "MisCaption This!" is a training dataset comprising LVLM-generated miscaptioned images.<n>"Latent Multimodal Reconstruction" (LAMAR) is a network trained to reconstruct the embeddings of truthful captions.<n>Experiments show that models trained on "MisCaption This!" better generalize on real-world misinformation.
• Score: 15.66049149213069
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multimodal misinformation, such as miscaptioned images, where captions misrepresent an image's origin, context, or meaning, poses a growing challenge in the digital age. To support fact-checkers, researchers have been focusing on creating datasets and developing methods for multimodal misinformation detection (MMD). Due to the scarcity of large-scale annotated MMD datasets, recent studies leverage synthetic training data via out-of-context image-caption pairs or named entity manipulations; altering names, dates, and locations. However, these approaches often produce simplistic misinformation that fails to reflect real-world complexity, limiting the robustness of detection models trained on them. Meanwhile, despite recent advancements, Large Vision-Language Models (LVLMs) remain underutilized for generating diverse, realistic synthetic training data for MMD. To address this gap, we introduce "MisCaption This!", a training dataset comprising LVLM-generated miscaptioned images. Additionally, we introduce "Latent Multimodal Reconstruction" (LAMAR), a network trained to reconstruct the embeddings of truthful captions, providing a strong auxiliary signal to the detection process. To optimize LAMAR, we explore different training strategies (end-to-end training and large-scale pre-training) and integration approaches (direct, mask, gate, and attention). Extensive experiments show that models trained on "MisCaption This!" generalize better on real-world misinformation, while LAMAR sets new state-of-the-art on both NewsCLIPpings and VERITE benchmarks; highlighting the potential of LVLM-generated data and reconstruction-based approaches for advancing MMD. We release our code at: https://github.com/stevejpapad/miscaptioned-image-reconstruction

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