Statistical Forensics

Guidelines and Testing for Image Manipulation

General Information

Image manipulation is the purposeful transformation or alteration of an image using various methods and techniques to achieve desired results. There are many types of ways one can manipulate images, however, the most common are splicing (lanes or features in images have been removed or rearranged) and extreme contrast adjustment. Though these two do not affect the results or data in a paper, these are still not good because it’s still an alteration to the real images. Other errors include: duplication of images in a multi-panel figure, erasing/editing parts of images, and enhancing contrast/color on a specific feature within a figure. These manipulated images usually come from cameras, blots (shows protein expression in an experimental sample), or gels (shows DNA or RNA expression in an experimental sample). 

To detect image manipulation, some journals that detect manipulation will ask the authors to provide original images and explanations for slight manipulations. If the authors are unable to do that, then one can suspect scientific and research misconduct. To combat image manipulation, journals can enforce strict policies and establish processes for reviewing images in accepted manuscripts (ISMTE, 2017). In addition, there is software and applications that one could run the image through to detect image manipulation, such as Adobe Bridge and ImageJ. One could also use Google Images to see if an image originated elsewhere or if there are copies of an image elsewhere in the case of plagiarism. Lastly, Microsoft PowerPoints photo editor can allow one to see if there are any original underlying images underneath the image being shown. For more information on detecting image manipulation, please refer to the sources below. 

Here are some resources on the technique:

Abraham, E. (2008). The ATS Journals’ policy on image manipulation. ATS Journals, 5(9).

Adler J. Veracity of raw images can also come into question. Nature. 2005;435(7043):736.

American Academy of Dermatology. Guide for Authors. Journal of the American Academy of Dermatology.

Annonio, M. (2008). Image manipulation and the editor: Tools to prevent unacceptable alterations. Aegis Peer Review Management.

Boss, J. M. (2010). What Do You Mean, I Already Published It! Ethics in Scientific Publishing. American Association of Immunologists Newsletter.

Bucci, E.M. (2018).  Automatic detection of image manipulations in the biomedical literature. Cell death & disease, 9(3), 1-9.

Carniol, K. (2015). Common pitfalls in figure preparation. CellPress.

Castillo M. Digital forensics and the American Journal of Neuroradiology. American Journal of Neuroradiology. 2008;29(2):211–212.

Cell Press Digital Image Guidelines. Cell Press.

Chaffin, W. L. (2016). What’s wrong with this picture?Responsible Conduct of Research and Academic Integrity Conference.

Clarke, M. (2006). Ethics: Detecting misconduct. Nature Blogs.

Cromey, D. (2013). Digital imaging: ethics. Southwest Environmental Health Sciences Center, University of Arizona.

Cromey, D.W. (2012). Digital images are data and should be treated as such. In Taatjes, D.J. & Roth, J. (Eds.), Cell imaging techniques: methods and protocols (pp. 1-27).

Cromey, D. W. (2010). Avoiding twisted pixels: ethical guidelines for the appropriate use and manipulation of scientific digital images. Science and engineering ethics, 16(4), 639-667.

Digital Image Forensic Analyzer

Editorial and Publishing Policies: Image integrity and standards. Nature.

Ethical Image Editing. Microscopic Imaging Core Facility and Confocal Microscopy Center, Wake Forest University.

Farid, H. (2017). How to Detect Faked Photos. American Scientist, 105(2).

Forensic Tools. The Office of Research Integrity.

Forensic Images Samples. The Office of Research Integrity.

Frow, E.K. (2012). Drawing a line: Setting guidelines for digital image processing in scientific journal articles. Social Studies of Science, 42(3).

Gel slicing and dicing: a recipe for disaster. (2004). Nature Cell Biology, 6(275).

Image Data Integrity – consulting services.

Image Manipulation: the Do’s and Don’ts. LetPub.

Jernstedt, J. (2014). Trust and scientific publication. AJB policy for digital images. American Journal of Botany, 101(12).

Johnson, J. (2012). Not seeing is not believing: improving the visibility of your fluorescence images. Molecular Biology of the Cell, 23(5).

Lang, T.A., Talerico, C., and Siontis, C.M. (2012). Documenting Clinical and Laboratory Images in Publications: The CLIP Principles. CHEST.

Martin, C., & Blatt, M. (2013). Manipulation and misconduct in the handling of image data. The Plant Cell, 25(9), 3147-3148.

MSA Policy on Digital Imaging. (2003). Microscopy Society of America.

Mudrak, B. Avoiding image fraud: 7 rules for editing images. American Journal Experts.

Nature Cell Biology Editorial (2006).  Appreciating data: warts, wrinkles and all. Nature.

Newman, A. (2013). The art of detecting data and image manipulation.

North, A.J. (2006). Seeing is believing? A beginners’ guide to practical pitfalls in image acquisition. The Journal of Cell Biology, 172(1), 9-18.

Online Learning Tool for Research Integrity and Image Processing. The Office of Research Integrity.

Presentation by Ken Yamada, NIDCR, NIH, and Liz Williams, Journal of Cell Biology, at NIH Workshop on Reproducibility of Data Collection and Analysis. Modern Technologies in Cell Biology: Potentials and Pitfalls, November 24, 2014 (2:59:00 – 3:54:13).

Rallision, S. (2014). Publishing images: Is technology progressing faster than our ethics? Physiology News Magazine. 

Rossner, M. (2002).  Figure manipulation: assessing what is acceptable.  Journal of Cell Biology 158 (7): 1151.

Rossner, M. & Yamada, K.M. (2004). What’s in a picture? The temptation of image manipulation. The Journal of Cell Biology, 166(1), 11-15.

Rossner, M. (2006). How to guard against image fraud. The Scientist.

Rossner, M. (2012). Digital images and misconduct. In The white paper on publication ethics (3.4). Council of Science Editors.

Sweet, D. (2015). Checking out our figures. CellPress.

The Microscopy Alliance. Digital Image Ethics. The University of Arizona.

​Williams, C.L., Casadevall, A., and Jackson, S. (2019).  Figure errors, sloppy science, and fraud: keeping eyes on your data. The Journal of Clinical Investigation.