Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
British Machine Vision Conference (BMVC)
As autonomous driving and augmented reality evolve a practical concern is data privacy, notably when these applications rely on user image-based localization. The widely adopted technology uses local feature descriptors derived from the images. While it was long thought that they could not be reverted back, recent work has demonstrated that under certain conditions reverse engineering attacks are possible and allow an adversary to reconstruct RGB user images. This poses a potential risk to user privacy.
We take this further and model potential adversaries using a privacy threat model. We show a reverse engineering attack on sparse feature maps under controlled conditions and analyze the vulnerability of popular descriptors including FREAK, SIFT and SOSNet. Finally, we evaluate potential mitigation techniques that select a subset of descriptors to carefully balance privacy reconstruction risk. While preserving image matching accuracy, our results show that similar accuracy can be obtained when revealing less information.
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu
Ilkan Esiyok, Pascal Berrang, Katriel Cohn-Gordon, Robert Künnemann