2 research outputs found
Snapshot Difference Imaging using Time-of-Flight Sensors
Computational photography encompasses a diversity of imaging techniques, but
one of the core operations performed by many of them is to compute image
differences. An intuitive approach to computing such differences is to capture
several images sequentially and then process them jointly. Usually, this
approach leads to artifacts when recording dynamic scenes. In this paper, we
introduce a snapshot difference imaging approach that is directly implemented
in the sensor hardware of emerging time-of-flight cameras. With a variety of
examples, we demonstrate that the proposed snapshot difference imaging
technique is useful for direct-global illumination separation, for direct
imaging of spatial and temporal image gradients, for direct depth edge imaging,
and more
Key-Nets: Optical Transformation Convolutional Networks for Privacy Preserving Vision Sensors
Modern cameras are not designed with computer vision or machine learning as
the target application. There is a need for a new class of vision sensors that
are privacy preserving by design, that do not leak private information and
collect only the information necessary for a target machine learning task. In
this paper, we introduce key-nets, which are convolutional networks paired with
a custom vision sensor which applies an optical/analog transform such that the
key-net can perform exact encrypted inference on this transformed image, but
the image is not interpretable by a human or any other key-net. We provide five
sufficient conditions for an optical transformation suitable for a key-net, and
show that generalized stochastic matrices (e.g. scale, bias and fractional
pixel shuffling) satisfy these conditions. We motivate the key-net by showing
that without it there is a utility/privacy tradeoff for a network fine-tuned
directly on optically transformed images for face identification and object
detection. Finally, we show that a key-net is equivalent to homomorphic
encryption using a Hill cipher, with an upper bound on memory and runtime that
scales quadratically with a user specified privacy parameter. Therefore, the
key-net is the first practical, efficient and privacy preserving vision sensor
based on optical homomorphic encryption.Comment: BMVC'20 (Best Paper - Runner up