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As thousands of images are processed at a mass scale, the threat may apply to most users of digital cameras. We consider a state-of-the-art algorithm for digital camera identification proposed in Lucas et al. (IEEE Trans Inf Forensics Secur 1(2):205\u2013214, 2006) and discuss strategies that can be used to bypass it, in order to make information about the camera unavailable. It turns out that many natural strategies like Gaussian blur, adding artificial noise or removing pixels\u2019 least significant bit from the image does not prevent the identification of a camera unless a huge loss of image details is suffered. On the other hand, we show a method to bypass the camera identification with a just marginally more complex, yet not intuitive, method namely cropping the image on the edges and resizing to the original size using Lanczos resampling.<\/jats:p>","DOI":"10.1007\/s11042-020-09133-9","type":"journal-article","created":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T12:02:31Z","timestamp":1599307351000},"page":"921-942","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["On robustness of camera identification algorithms"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4488-3488","authenticated-orcid":false,"given":"Jaros\u0142aw","family":"Bernacki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,5]]},"reference":[{"key":"9133_CR1","doi-asserted-by":"crossref","unstructured":"Bernacki J, Klonowski M, Syga P (2017) Some remarks about tracing digital cameras \u2013 faster method and usable countermeasure. 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