פרטים יפורסמו בהמשך

סמינר זה יחשב כסמינר שמיעה לתלמידי תואר שני

You are invited to attend a lecture on Tuesday, September 5, 2023 13:00

https://tau-ac-il.zoom.us/j/83680862693?pwd=OFI3SlVWc3hTcW1BVi9rTndHTE1FQT09

Detection and Recovery of Hidden Submatrices

By:

Marom Dadon

MSc. student under the supervision of Dr. Wasim Huleihel and Dr. Tamir Bendory

ABSTRACT

We study the problems of detection and recovery of hidden submatrices with elevated means inside a large Gaussian random matrix. We consider two different structures for the planted submatrices. In the first model, the planted matrices are disjoint, and their row and column indices can be arbitrary. Inspired by scientific applications, the second model restricts the row and column indices to be consecutive. In the detection problem, under the null hypothesis, the observed matrix is a realization of independent and identically distributed standard normal entries. Under the alternative, there exists a set of hidden submatrices with elevated means inside the same standard normal matrix. Recovery refers to the task of locating the hidden submatrices. For both problems, and for both models, we characterize the statistical and computational barriers by deriving information-theoretic lower bounds, designing and analyzing algorithms matching those bounds, and proving computational lower bounds based on the low-degree polynomials conjecture. In particular, we show that the space of the model parameters (i.e., number of planted submatrices, their dimensions, and elevated mean) can be partitioned into three regions: the impossible regime, where all algorithms fail; the hard regime, where while detection or recovery are statistically possible, we give some evidence that polynomial-time algorithm do not exist; and finally the easy regime, where polynomial-time algorithms exist.

סמינר זה יחשב כסמינר שמיעה לתלמידי תואר שני

You are invited to attend a lecture on 3^rd September 2023 at 10:00

Kitot Building, Room 011

Join Zoom Meeting

https://tau-ac-il.zoom.us/j/6690388255

Encoding Facial Behavior in Videos for Identification Purposes

By:

Mor-Avi Azulay

MSc student under the supervision of Prof. David Mendlovic and Dr. Dan Raviv

Abstract

Facial appearance-based methods have settled as state-of-the-art approaches for the task of human re-identification. However, the captured appearance can change greatly under different environmental conditions or cameras. Most methods base their identification upon static data, i.e., one or more representations of single frames. Hence, dynamic data such as temporal changes which convey meaningful identity-unique information, i.e., facial motion patterns, are omitted.

The research conducted in this thesis focuses on a novel approach to extract facial behavior characteristics from a video and encode it into a light representation we call Motion-model, which acts as a dictionary of the typical behavior for different expression-based states, or states transition, within a high-dimensional embedding space, and can naturally modeled as a graph. We further present MotionDNANet, a deep graph-based neural network with a dedicated architecture that learns a layer-specific graph adjacency matrix, to process Motion-models for identification purposes.

Experimental results over the large-scale VoxCeleb2 dataset, show that our facial behavior-based method offers a better user experience in highly secured systems that require a low false acceptance rate (FAR) where the true acceptance rate (TAR) drops dramatically for other tested methods. I.e., our TAR at FAR=0.1% is 87.48% and all others are below 15%. Moreover, results over the VideoForensicsHQ dataset show that our Motion-model concept can help detect false identifications in the case of DeepFake videos.