Human privacy can be easily violated with surveillance camera footage that constantly captures public space, and therefore the people in it. Because the identity of persons is rarely important in the surveillance camera service, it is desirable to remove the identities of persons from the recordings. The simplest method for this is de-identification aided by artificial intelligence and computer vision. There are many methods, some very simple, others quite demanding to implement, and it is necessary to choose the best one given the circumstances. Surveillance camera images are not known for their high fidelity, so the testing was conducted on a set of images that are of poorer quality, and thus affect the test results. Because of their otherwise fairly simple nullification, many write off simple de-identification methods (so-called naive methods) such as facial blur (Gaussian filter) or pixelation, but in a case like this, when it comes to low-quality images, such an approach may pay off due to its simple implementation and non-demanding computational complexity. In several cases, the effectiveness of naive de-identification versus FaceNet-based InceptionResnet V1 face recognition network was examined. It turned out that even with the use of otherwise effective methods of reversing naive de-identification methods, the face recognition model did not have much success, precisely because of the low quality of the images used.