# Determining Intent of Changes to Ascertain Fake Crowdsourced Image Services

arXiv: https://arxiv.org/abs/2403.12045 · HTML: https://mlsanity.com/paper/2403.12045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 11 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Content-Aware Detection of Temporal Metadata Manipulation](https://mlsanity.com/paper/2103.04736/) (2021)
- [Fake Colorized Image Detection](https://mlsanity.com/paper/1801.02768/) (2018)
- [Social-Sensor Composition for Tapestry Scenes](https://mlsanity.com/paper/2003.13684/) (2020)
- [Fake-image detection with Robust Hashing](https://mlsanity.com/paper/2102.01313/) (2021)
- [Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction](https://mlsanity.com/paper/2009.11663/) (2020)
- [Detecting Changes in Crowdsourced Social Media Images](https://mlsanity.com/paper/2310.16848/) (2023)

## Abstract (arXiv)

We propose a novel framework for crowdsourced images to determine the likelihood of an image being fake. We use a service-oriented approach to model and represent crowdsourced images uploaded on social media, as image services. Trust may, in some circumstances, be determined by using only the non-functional attributes of an image service, i.e., image metadata. We define intention of changes as a key parameter to ascertain fake image services. A novel framework is proposed to estimate the intention of underlying changes considering change in semantics of an image. Our experiments show high accuracy using a large real dataset.