數據增強采用隨機高斯噪聲，JPRG壓縮，隨機翻轉，使數據集一變多
拼接
MS COCO是用於目標檢測，語義分割的數據集。包括82783訓練圖像，40504測試圖像
CASIA v2.0：包含7,491個真實圖像和5,123個偽造（拼接和復制移動）彩色圖像，尺寸范圍為240 × 160至900 × 600 JPEG和TIFF格式
CASIA：包含TIFF格式的圖像1275對(篡改圖像1275個，原始圖像1275個)，其中大部分像素分辨率約為384×256 。拼接的偽造區域是小而精細的對象
Columbia gray DVMM：由933個真實圖像和912個BMP格式的拼接圖像組成，大小均為128 × 128像素，沒有任何后處理.
DSO-1：由100個具有像素方向真實情況的拼接圖像和100個原始圖像組成，分辨率為2,048 × 1,536像素
COLUMB：包含179對TIFF格式圖像，大小757×568。拼接偽造區域是簡單、大、無意義的區域
FORENSICS：由高分辨率圖像(2018×1536)組成，包含144對PNG格式的圖像。拼接的偽造區域是小而精細的對象，但是這些區域更真實，更接近背景。
PS-Battles：
The Reddit dataset：本文收集9947個可用的圖像對。（Image provenance analysis at scale 的作者從在線社區收集了reddit 數據集）
MFC2018：
carvalho[]：94幅圖像
Realistic Tampering[]：220圖像，拼接后還有后處理操作。這個數據集中也有復制粘貼圖像
參考：
CASIA v2.0：J. Dong and W. Wang. (2011). CASIA Tampered Image Detection Evalua-tion (TIDE) Database, v1.0 and v2.0. [Online]. Available: http://forensics.idealtest.org/
Columbia gray DVMM：T.-T. Ng, J. Hsu, and S.-F. Chang. Columbia Image Splicing Detection Evaluation Dataset. Accessed: Sep. 19, 2019. [Online]. Available:http://www.ee.columbia.edu/ln/dvmm/downloads/AuthSplicedDataSet/AuthSplicedDataSet.htm
DSO-1：T. J. de Carvalho, C. Riess, E. Angelopoulou, H. Pedrini, and A. Rocha,
``Exposing digital image forgeries by illumination color classification,''
IEEE Trans. Inf. Forensics Security, vol. 8, no. 7, pp. 1182-1194, Jul. 2013.
COLUMB：Y.-F. Hsu , S.-F. Chang , Detecting image splicing using geometry invariants and camera characteristics consistency, in: Proceedings of the IEEE Interna- tional Conference on Multimedia and Expo, IEEE, 2006, pp. 549–552 .
FORENSICS： https://signalprocessingsociety.org/newsletter/2014/01/ieee- ifs- tc- image- forensics- challenge- website- new- submissions (2014)
復制粘貼
CMH：由四個CMH子數據集組成，共有108張復制-移動篡改圖像。篡改圖像包含了旋轉和縮放變換的攻擊
MICC-F200：有110個基礎和110個篡改圖像。圖像的大小范圍從722×480至800×600。然而，該數據集不提供篡改圖像的 ground truth
MICC-F600：由160幅篡改圖像和440幅原始圖像組成，圖像分辨率從800×533到3888×2592不等
MICC-2000：有1300個基礎和700張篡改圖像，尺寸為2048×1536。然而，該數據集也不提供篡改圖像的 ground truth
GRIP：有80個基本圖像和80個相應的篡改圖像。大小相同都是 768×1024。數據集提供了相應的ground truth。大部分復制的片段很光滑。
Coverage：數據集有100個基本圖像和相應的篡改圖像，平均尺寸為400×486
SUN：數據集有397個類別和108,754個基本圖像。相應的注釋圖像可以方便地生成篡改圖像和 ground truth
FAU：數據集有48張高分辨率的基礎圖像和48張對應的具有真實復制-移動操作的篡改圖像，平均大小為3000×2300
CASIA：數據集有1309個復制-移動偽造圖像，其中包含了旋轉、縮放變換的攻擊。
CoMoFoD：數據集有200個基本圖像，平均大小為512×512
MFC2018： Media forensicschallenge 2018 包含1327正樣本對和16673負樣本對
PS-Battles：共102028圖像，11142個子類。（The ps-battles dataset - an image collection for image manipulation detection的作者收集而來）
參考
FAU： V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, “An evaluation of popular copy-move forgery detection approaches,” IEEE Trans. on Inf. Forensics and Security, vol. 7, no. 6, pp. 1841–1854, 2012
GRIP： D. Cozzolino, G. Poggi, and L. Verdoliva, “Efficient dense-field copymove forgery detection,” IEEE Trans. on Inf. Forensics and Security, vol. 10, no. 11, pp. 2284–2297, 2015
MICC-F200： I. Amerini, L. Ballan, R. Caldelli, A. Del Bimbo, and G. Serra, “A SIFT-based forensic method for copy–move attack detection and transformation recovery,” IEEE Trans. on Inf. Forensics and Security, vol. 6, no. 3, pp. 1099–1110, 2011
MICC-600： I. Amerini, L. Ballan, R. Caldelli, A. D. Bimbo, L. D. Tongo, and G. Serra, “Copy-move forgery detection and localization by means of robust clustering with j-linkage,” Signal Process.: Image Commun., vol. 28, no. 6, pp. 659 – 669, 2013
CMH：E. Silva, T. Carvalho, A. Ferreira, and A. Rocha, “Going deeper into copy-move forgery detection: Exploring image telltales via multi-scale analysis and voting processes,” J. Vis. Commun. and Image Represent., vol. 29, pp. 16 – 32, 2015
COVERAGE：B. Wen, Y. Zhu, R. Subramanian, T. Ng, X. Shen, and S. Winkler, “COVERAGE - a novel database for copy-move forgery detection,” in Proc. IEEE Int. Conf. Image Process., 2016, pp. 161–165.
SUN：J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “SUN database: Large-scale scene recognition from abbey to zoo,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 3485–3492
CoMoFoD：D. Tralic, I. Zupancic, S. Grgic, and M. Grgic, “CoMoFoD—New database for copy-move forgery detection,” in Proc. ELMAR, Sep. 2013, pp. 49–54.