With the proliferation of digital visual data in diverse domains (video surveillance, social networks, medias, etc.), privacy concerns increase. Obscuring faces in images and videos is one option to preserve privacy while keeping a certain level of quality and intelligibility of the video. Most popular filters are blackener (black masking), pixelization and blurring. Even if it appears efficient at first sight, in terms of human perception, we demonstrate in this article that as soon as the category and the strength of the filter used to obscure faces can be (automatically) identified, there exist in the literature ad-hoc powerful approaches enable to partially cancel the impact of such filters with regards to automatic face recognition. Hence, evaluation is expressed in terms of face recognition rate associated with clean, obscured and de-obscured face images.