6 accepted papers
Over the years, the forensics community has proposed several deep learning-based detectors to mitigate the risks of generative AI. Recently, frequency-domain artifacts (particularly periodic peaks in the magnitude spectrum), have received significant attention, as they have been often considered a s…
Federated Learning (FL) is a promising solution for training machine learning models on data that may contain personal or sensitive information, allowing different data-owners to provide local training updates to a shared model while keeping their data on their own premises. To protect the model fro…
More complex and ever more common lens distortion correction post-processing is seriously hampering state-of-the-art camera attribution techniques. In this paper, we show that the two main existing techniques, namely PRNU (Photo Response Non Uniformity)-based and linear-pattern-based, can be success…
In this work, we address the problem of designing delay-based anonymous communication systems. We consider a timed mix where an eavesdropper wants to learn the communication pattern of the users, and study how the mix must delay the messages so as to increase the adversary's estimation error. We sho…
Recent advances in Next Generation Sequencing have increased the availability of genomic data for more accurate analyses, like testing for the genetic susceptibility to a disease. Current laboratories' facilities cannot cope with this data growth, and genomic processing needs to be outsourced, compr…
Images are inherently sensitive signals that require privacy-preserving solutions when processed in an untrusted environment, but their efficient encrypted processing is particularly challenging due to their structure and size. This work introduces a new cryptographic hard problem called m-RLWE (mul…