# Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media

arXiv: https://arxiv.org/abs/2203.07167 · HTML: https://mlsanity.com/paper/2203.07167/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Detecting and Simulating Artifacts in GAN Fake Images](https://mlsanity.com/paper/1907.06515/) (2019)
- [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](https://mlsanity.com/paper/1312.6229/) (2013)
- [Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking](https://mlsanity.com/paper/1803.11285/) (2018)
- [Do GANs leave artificial fingerprints?](https://mlsanity.com/paper/1812.11842/) (2018)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [Detecting GAN-generated Imagery using Color Cues](https://mlsanity.com/paper/1812.08247/) (2018)
- [On the Origins of Memes by Means of Fringe Web Communities](https://mlsanity.com/paper/1805.12512/) (2018)
- [On the generalization of GAN image forensics](https://mlsanity.com/paper/1902.11153/) (2019)
- [BreakingNews: Article Annotation by Image and Text Processing](https://mlsanity.com/paper/1603.07141/) (2016)
- [Image Provenance Analysis at Scale](https://mlsanity.com/paper/1801.06510/) (2018)
- [MemeSequencer: Sparse Matching for Embedding Image Macros](https://mlsanity.com/paper/1802.04936/) (2018)
- [CNN features are also great at unsupervised classification](https://mlsanity.com/paper/1707.01700/) (2017)
- [Provenance Filtering for Multimedia Phylogeny](https://mlsanity.com/paper/1706.00447/) (2017)

## Built on by (most cited first, 2 of 2)

- [Improving Model Evaluation using SMART Filtering of Benchmark Datasets](https://mlsanity.com/paper/2410.20245/) (2024)
- [Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale](https://mlsanity.com/paper/2206.00282/) (2022)

## Abstract (arXiv)

The massive spread of visual content through the web and social media poses both challenges and opportunities. Tracking visually-similar content is an important task for studying and analyzing social phenomena related to the spread of such content. In this paper, we address this need by building a dataset of social media images and evaluating visual near-duplicates retrieval methods based on image retrieval and several advanced visual feature extraction methods. We evaluate the methods using a large-scale dataset of images we crawl from social media and their manipulated versions we generated, presenting promising results in terms of recall. We demonstrate the potential of this method in two case studies: one that shows the value of creating systems supporting manual content review, and another that demonstrates the usefulness of automatic large-scale data analysis.