Modern Fingerprint Presentation Attack Detection (FPAD) modules have been particularly successful in avoiding attacks exploiting artificial fingerprint replicas against Automated Fingerprint Identification Systems (AFISs). As for several other domains, Machine and Deep Learning strongly contributed to this success, with all recent state-of-the-art detectors leveraging learning-based approaches. An insidious flip side is represented by adversarial attacks, namely, procedures intended to mislead a target detector. Indeed, despite this type of attack has been considered unrealistic, as it presupposes access to the communication channel between the sensor and the detector, in a recent work, we have highlighted the possibility of transferring a fingerprint adversarial attack from the digital domain to the physical one. In this work, we take a step further by introducing a new procedure designed to make the physical adversarial presentation attack i) more robust to the physical crafting of the PAI by exploiting explainability techniques, ii) easier to adapt to different fingerprint scanners and adversarial algorithms, and iii) usable in a black-box scenario. To quantify the impact of these novel adversarial presentation attacks family, designed to be robust to the physical crafting process, we assess the performance of both state-of-the-art PAD modules alone and integrated AFISs. Results highlight the approach’s feasibility, opening a new series of threats in the context of fingerprint PAD.