8,501 research outputs found
The eyes know it: FakeET -- An Eye-tracking Database to Understand Deepfake Perception
We present \textbf{FakeET}-- an eye-tracking database to understand human
visual perception of \emph{deepfake} videos. Given that the principal purpose
of deepfakes is to deceive human observers, FakeET is designed to understand
and evaluate the ease with which viewers can detect synthetic video artifacts.
FakeET contains viewing patterns compiled from 40 users via the \emph{Tobii}
desktop eye-tracker for 811 videos from the \textit{Google Deepfake} dataset,
with a minimum of two viewings per video. Additionally, EEG responses acquired
via the \emph{Emotiv} sensor are also available. The compiled data confirms (a)
distinct eye movement characteristics for \emph{real} vs \emph{fake} videos;
(b) utility of the eye-track saliency maps for spatial forgery localization and
detection, and (c) Error Related Negativity (ERN) triggers in the EEG
responses, and the ability of the \emph{raw} EEG signal to distinguish between
\emph{real} and \emph{fake} videos.Comment: 8 page
Cell sorting in a Petri dish controlled by computer vision.
Fluorescence-activated cell sorting (FACS) applying flow
cytometry to separate cells on a molecular basis is a widespread
method. We demonstrate that both fluorescent and unlabeled live
cells in a Petri dish observed with a microscope can be
automatically recognized by computer vision and picked up by a
computer-controlled micropipette. This method can be routinely
applied as a FACS down to the single cell level with a very
high selectivity. Sorting resolution, i.e., the minimum distance
between two cells from which one could be selectively removed
was 50-70 micrometers. Survival rate with a low number of 3T3
mouse fibroblasts and NE-4C neuroectodermal mouse stem cells was
66 +/- 12% and 88 +/- 16%, respectively. Purity of sorted
cultures and rate of survival using NE-4C/NE-GFP-4C co-cultures
were 95 +/- 2% and 62 +/- 7%, respectively. Hydrodynamic
simulations confirmed the experimental sorting efficiency and a
cell damage risk similar to that of normal FACS
A novel haptic model and environment for maxillofacial surgical operation planning and manipulation
This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone
Visualizing Object Oriented Software in Three Dimensions
There is increasing evidence that it is possible to perceive and understand increasingly comple x information systems if they are displayed a s graphical objects in a three dimensional space . Object-oriented software provides an interestin g test case - there is a natural mapping fro m software objects to visual objects . In this paper we explore two areas. 1) Information perception : we are running controlled experiments to determine empirically if our initial premise is valid; how much more (or less) can be understoo d in 3D than in 2D? 2) Layout: our strategy is to combine partially automatic layout with manua l layout. This paper presents a brief overview of the project, the software architecture and some preliminary empirical results
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection
Manipulated videos often contain subtle inconsistencies between their visual
and audio signals. We propose a video forensics method, based on anomaly
detection, that can identify these inconsistencies, and that can be trained
solely using real, unlabeled data. We train an autoregressive model to generate
sequences of audio-visual features, using feature sets that capture the
temporal synchronization between video frames and sound. At test time, we then
flag videos that the model assigns low probability. Despite being trained
entirely on real videos, our model obtains strong performance on the task of
detecting manipulated speech videos. Project site:
https://cfeng16.github.io/audio-visual-forensicsComment: CVPR 202
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