Datasets:

harshitajainn
/

coco-cmfd

license: cc-by-4.0
task_categories:
  - image-segmentation
  - image-classification
language:
  - en
tags:
  - image-forensics
  - copy-move-forgery
  - forgery-detection
  - tamper-detection
  - synthetic
  - coco
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*.parquet

COCO-CMFD

A synthetic copy-move forgery dataset generated from MS-COCO 2017, with source/target-separated ground truth for copy-move forgery detection (CMFD).

Each sample takes one annotated COCO object, applies a mild affine transform, and pastes it elsewhere in the same image at a location that passes scene-plausibility checks (support surface, horizon band, perspective scale, occlusion). Ground truth is provided as a 3-class trimap, a binary mask, a 16 px patch-label grid, and a hard-negative mask of the scene's other objects.

Code, generation pipeline and quality tooling: https://github.com/harshitajain523/COCO-Copy-Move-Forgery-Dataset

Dataset details


Samples	12,271
Source images	7,150 distinct COCO train2017 images (1–8 samples each, mean 1.7)
Categories	64 COCO categories across 9 supercategories
Resolution	native COCO, 320–640 px per side
Pasted region size	2.5%–47.6% of image area (median 8.6%)
Transforms	scale 0.70–1.45, rotation ±10°, horizontal flip on 25.1%
Post-processing	none (no JPEG, noise or blur applied)

Fields

Field	Type	Description
`image`	Image	Forged image (PNG, lossless)
`trimap`	Image	`0` background, `128` source, `255` target
`binary_mask`	Image	`0`/`255`, source ∪ target
`hard_negatives`	Image	`0`/`255`, all non-source annotated objects
`patch_labels`	list[int8]	Flattened 16 px grid: `-1` ignore, `0` bg, `1` source, `2` target
`patch_labels_shape`	list[int]	`[H/16, W/16]` for reshaping `patch_labels`
`gate_profile`	string	`strict`, `relaxed_v1`, or `relaxed_v2` (see below)
`source_reused`	int	`1` if this source object is also the source of another sample from the same image
`source_category_name`	string	COCO category of the copied object
`scale_factor`, `rotation_angle`, `flip`	float/int	Transform applied to the copy
`source_bbox`, `target_bbox`	string	`(x, y, w, h)` tuples
`placement_tier`, `horizon_tier`	string	Which candidate pool / vertical constraint accepted the placement

24 metadata fields in total; image_id refers to the originating COCO image.

Patch labels use a strict purity rule: a patch is labelled only if all 256 pixels share one class, otherwise -1. This keeps the copy-move boundary out of patch-level contrastive objectives.

Generation profiles

`gate_profile`	Samples	Source-object gates
`strict`	4,420	edge margin 15 px, isolation 5×5, bbox fill ≥ 0.25, paste ≥ 2% of image
`relaxed_v1`	166	edge 8 px, isolation 3×3, fill ≥ 0.20
`relaxed_v2`	7,685	as `relaxed_v1`, plus paste ≥ 1.2% of image

Scene-plausibility gates are identical across all three profiles; only source-object selectivity differs. Filter on gate_profile == "strict" for the most conservative subset.

Usage

from datasets import load_dataset
import numpy as np

ds = load_dataset("harshitajainn/coco-cmfd", split="train")
s = ds[0]

image = s["image"]                              # PIL.Image
trimap = np.array(s["trimap"])
source_mask, target_mask = trimap == 128, trimap == 255
patches = np.array(s["patch_labels"], dtype=np.int8).reshape(
    s["patch_labels_shape"]
)

WebDataset tar shards are also published under wds/ for streaming-heavy pipelines; each sample key carries .png, .trimap.png, .binary.png, .hardneg.npy, .patches.npy and .json.

Verification

Run on the released set with the tooling in the GitHub repository:

Mask alignment (60 samples): IoU between changed pixels and the labelled target region — mean 1.0000, min 0.9997, with 0 pixels modified outside any mask. Blending uses an inward-only 2 px feather, so the alpha ramp stays inside the labelled region and the ground truth is exact rather than approximate.
Integrity: 61,355/61,355 referenced files present, 400 sampled pixel checks pass.
Post-hoc plausibility audit: 12,190 clean / 81 rejected (0.66%).

Intended use and limitations

Intended for pretraining and evaluating copy-move forgery detectors, including source/target discrimination and patch-level contrastive objectives.

Limitations:

Synthetic. Forgeries are algorithmically composited, not made by a human with intent to deceive. Detectors trained only on this data should be fine-tuned and evaluated on real-world benchmarks.
No post-processing. Images are lossless PNG with no JPEG recompression, noise or blur. Robustness to those degradations is not exercised by this set.
Mild transforms only (±10° rotation, 0.70–1.45× scale). Large rotations and extreme rescaling are out of distribution.
Category imbalance follows COCO: person 8.1%, bird 7.5%, clock 7.2% are the most frequent of 64 categories.
Source correlation: 4,050 samples reuse a source object that another sample from the same image also uses. Group by image_id when constructing splits to avoid leakage.
Single copy-move per image; no multi-source or nested forgeries.

License and attribution

Released under CC BY 4.0.

Images derive from MS-COCO 2017. COCO annotations are CC BY 4.0; COCO images originate from Flickr and remain subject to their owners' terms — the COCO Consortium does not hold their copyright. The CC BY 4.0 grant covers the forgery generation, ground-truth masks and metadata, not the underlying photographic content. Review the COCO terms of use before redistributing the imagery, particularly for commercial use.

Citation

@dataset{jain_coco_cmfd_2026,
  author    = {Jain, Harshita},
  title     = {{COCO-CMFD}: A Synthetic Copy-Move Forgery Dataset
               with Source/Target Ground Truth},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {TODO},
  url       = {https://github.com/harshitajain523/COCO-Copy-Move-Forgery-Dataset}
}

Please also cite MS-COCO:

@inproceedings{lin2014microsoft,
  title     = {Microsoft {COCO}: Common Objects in Context},
  author    = {Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and
               Hays, James and Perona, Pietro and Ramanan, Deva and
               Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle = {ECCV},
  year      = {2014}
}