mlsanityData to 14 Oct 2026

Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text

arXiv 1707.01606 ↗First posted 6 Jul 2017cs.MM cs.AI cs.CVcited by 28

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Abstract (arXiv)

Real world multimedia data is often composed of multiple modalities such as an image or a video with associated text (e.g. captions, user comments, etc.) and metadata. Such multimodal data packages are prone to manipulations, where a subset of these modalities can be altered to misrepresent or repurpose data packages, with possible malicious intent. It is, therefore, important to develop methods to assess or verify the integrity of these multimedia packages. Using computer vision and natural language processing methods to directly compare the image (or video) and the associated caption to verify the integrity of a media package is only possible for a limited set of objects and scenes. In this paper, we present a novel deep learning-based approach for assessing the semantic integrity of multimedia packages containing images and captions, using a reference set of multimedia packages. We construct a joint embedding of images and captions with deep multimodal representation learning on the reference dataset in a framework that also provides image-caption consistency scores (ICCSs). The integrity of query media packages is assessed as the inlierness of the query ICCSs with respect to the reference dataset. We present the MultimodAl Information Manipulation dataset (MAIM), a new dataset of media packages from Flickr, which we make available to the research community. We use both the newly created dataset as well as Flickr30K and MS COCO datasets to quantitatively evaluate our proposed approach. The reference dataset does not contain unmanipulated versions of tampered query packages. Our method is able to achieve F1 scores of 0.75, 0.89 and 0.94 on MAIM, Flickr30K and MS COCO, respectively, for detecting semantically incoherent media packages.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jul 2017.

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Citing papers per year: 2021: 4; 2022: 0; 2023: 6; 2024: 10; 2025: 7; 2026: 1.

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  1. 2021NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Mediacited by 107
  2. 2024SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detectioncited by 74
  3. 2021Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resourcescited by 71
  4. 2024MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMscited by 61
  5. 2023Multimodal Automated Fact-Checking: A Surveycited by 53
  6. 2024DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Expertscited by 43
  7. 2023VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Biascited by 39
  8. 2024FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMscited by 38
  9. 2023Synthetic Misinformers: Generating and Combating Multimodal Misinformationcited by 23
  10. 2023Support or Refute: Analyzing the Stance of Evidence to Detect Out-of-Context Mis- and Disinformationcited by 21
  11. 2023RED-DOT: Multimodal Fact-checking via Relevant Evidence Detectioncited by 18
  12. 2021Twitter-COMMs: Detecting Climate, COVID, and Military Multimodal Misinformationcited by 16
16 more
  1. 2024MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Modelscited by 15
  2. 2024Multimodal Misinformation Detection by Learning from Synthetic Data with Multimodal LLMscited by 13
  3. 2024Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?cited by 11
  4. 2023Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Modelcited by 8
  5. 2024Exposing Text-Image Inconsistency Using Diffusion Modelscited by 4
  6. 2024"Image, Tell me your story!" Predicting the original meta-context of visual misinformationcited by 4
  7. 2025T^2Agent A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Searchcited by 4
  8. 2024Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformationcited by 2
  9. 2025Towards Robust and Realible Multimodal Misinformation Recognition with Incomplete Modalitycited by 2
  10. 2026RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wildcited by 2
  11. 2025Mitigating GenAI-Powered Evidence Pollution for Out-Of-Context Misinformation Detectioncited by 1
  12. 2025Latent Reconstruction from Generated Data for Multimodal Misinformation Detectioncited by 1
  13. 2025Out-of-Context Misinformation Detection via Variational Domain-Invariant Learning with Test-Time Trainingcited by 1
  14. 2021A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networkscited by 0
  15. 2025Towards Explainable Bilingual Multimodal Misinformation Detection and Localizationcited by 0
  16. 2025RW-Post: Auditable Evidence-Grounded Multimodal Fact-Checking in the Wildcited by 0