mlsanityData to 14 Oct 2026

TrainFors: A Large Benchmark Training Dataset for Image Manipulation Detection and Localization

arXiv 2308.05264 ↗First posted 10 Aug 2023cs.CVcited by 5

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Abstract (arXiv)

The evaluation datasets and metrics for image manipulation detection and localization (IMDL) research have been standardized. But the training dataset for such a task is still nonstandard. Previous researchers have used unconventional and deviating datasets to train neural networks for detecting image forgeries and localizing pixel maps of manipulated regions. For a fair comparison, the training set, test set, and evaluation metrics should be persistent. Hence, comparing the existing methods may not seem fair as the results depend heavily on the training datasets as well as the model architecture. Moreover, none of the previous works release the synthetic training dataset used for the IMDL task. We propose a standardized benchmark training dataset for image splicing, copy-move forgery, removal forgery, and image enhancement forgery. Furthermore, we identify the problems with the existing IMDL datasets and propose the required modifications. We also train the state-of-the-art IMDL methods on our proposed TrainFors1 dataset for a fair evaluation and report the actual performance of these methods under similar conditions.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 39 of its 82 references that are in the map. First posted on arXiv 10 Aug 2023.

Builds on

39 of 82 references.

References · 39

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27 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025NeXT-IMDL: Build Benchmark for NeXT-Generation Image Manipulation Detection & Localizationcited by 1
  2. 2026From Masks to Pixels and Meaning: A New Taxonomy, Benchmark, and Metrics for VLM Image Tamperingcited by 1
  3. 2026BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Imagescited by 0
  4. 2026Can Image Splicing and Copy-Move Forgery Be Detected by the Same Model? Forensim: An Attention-Based State-Space Approachcited by 0
  5. 2026ForensicsTok: Forensics-Guided Tokenized Modeling for Image Tampering Localizationcited by 0