Image Forensics: The Case of Image Forgery and Compression
Image forensics is a branch of digital forensic science pertaining to evidence found in images such as forgery that involves a legal image case. The literature is deficient on image forgery detection and localization methods that use a fraction of the image or one of its transformed bands. In this talk, we introduce a copulas-based image quality indexes that can detect and localize forgery using a single band of the image steerable pyramid, with a high efficiency. The simulation results showed almost perfect detect of more than one type of forgeries. Also, in this “talk” we present a generic, application independent lossless compression method for images consisting of a high number of discrete colors (less than 64 layers), such as digital maps and graphs. Our simulation results attained compression down to 0.035 bits per pixel which outperformed established and published methods.
Professor Saif alZahir received his PhD and MS degrees in Electrical and Computer Engineering from the University of Pittsburgh, Pennsylvania and the University of Wisconsin-Milwaukee in 1994 and 1984 respectively. He did his postdoc research at The University of British Columbia, UBC, Vancouver, BC, Canada. Dr. alZahir is involved in research in the areas of image processing, deep learning, forensic computing, data security, Networking, Corporate Governance, and Ethics. In 2003, The Innovation Council of British Columbia, Canada, named him British Columbia Research Fellow. He authored or co-authored more than 100 articles in peer reviewed journals and conferences, two books, and 5 book chapters. He is the founder and editor-in-chief of International Journal of Corporate Governance, London, England, (2008 – present), founder and editor-in-chief of the Signal Processing: International Journal, KL, Malaysia (2009 – 2018), Associate Editor, IEEE ACCESS – CTSoc. Dr. alZahir taught at seven universities in the US, Canada, and the Middle East including University of British Columbia, UBC, the University of Northern British Columbia, UNBC, where he was promoted to Full Professor in 2011, and The University of Alaska Anchorage, UAA. Dr. alZahir was the General-Chair of the IEEE International Symposium on Industrial Electronics, ISIE, June, 2022; the General-Chair of the IEEE – International Conference in Image Processing ICIP, 2021, and the General-Chair of the IEEE Nanotechnology Materials and Devices Conference (NMDC), September, 2015. Currently, he is the General-Chair of the IEEE IV 2023 (Intelligent Vehicles) June 4-7, 2023; the General Chair of the IEEE ISSPIT in October 2023 and the General-Chair of the IEEE – International Conference in Image Processing ICIP, 2025. In addition, Dr. alZahir has served on many editorial boards and on TPCs of many conferences.
Yerin Altında, Denizlerin Üstünde ve Uzayın Derinliklerinde: Görünür Spektrumun Ötesindeki Görüntüleme ile Dünyayı Keşfetmek
Hiperspektral kameralar, elektromanyetik spektrumun farklı dalga boylarından yüzlerce görüntü alan özel cihazlardır. Bu kapsamlı veri sayesinde her bir piksel çimen, su, altın, pamuk vb. şekilde malzeme içeriğine bağlı olarak etiketlenebilir. Hiperspektral görüntülerle sağlanan bu bilginin işlenmesi tarım, astronomi, tıbbi görüntüleme ve savunma gibi geniş uygulama alanlarında yer almaktadır. Bu sunumda, radar ve hiperspektral görüntüler kullanarak görünür spektrumun sınırlarını aşan ve yerin altını, denizlerin yüzeyini ve uzaydan bakarak dünyamızı gözlemlediğimiz araştırmalarımızı anlatacağım. Ayrıca, hiperspektral görüntüleme için derin gürültü azaltma ağları üzerine yürüttüğümüz mevcut araştırmalarımızı ve güncel trendleri tanıtacağım. Son olarak da görüntüleme sürecindeki paradigmaların değişimini ve cep telefonlarımıza yerleştirilecek düşük maliyetli programlanabilir hiperspektral kameralar ile görüntülemenin geleceğini tartışacağım.
Dr. Seniha Esen Yüksel, lisans derecesini 2003 yılında Orta Doğu Teknik Üniversitesi Elektrik ve Elektronik Mühendisliği Bölümü’nden, yüksek lisans derecesini 2005 yılında ABD'deki University of Louisville Elektrik ve Bilgisayar Mühendisliği Bölümü’nden ve doktora derecesini 2011 yılında ABD'deki University of Florida Bilgisayar Mühendisliği Bölümü’nden almıştır. Sonrasında, University of Florida Malzeme Bilimi Bölümü'nde doktora sonrası araştırmacı olarak ve Orta Doğu Teknik Üniversitesi Kuzey Kıbrıs Kampüsü'nde öğretim görevlisi olarak çalışmıştır. Halen, Hacettepe Üniversitesi Elektrik ve Elektronik Mühendisliği Bölümü’nde doçent olarak görev yapmaktadır. Aynı zamanda, Örüntü Tanıma ve Uzaktan Algılama Laboratuvarı'nın (PARRSLAB) direktörüdür. Araştırmaları, hiperspektral, radar, X-ışını, termal, SAR ve LiDAR gibi görünür spektrumun dışındaki dalgaboylarında çalışan sensörlerle makine öğrenimi ve bilgisayarlı görü üzerinedir. Dr. Yüksel, IEEE Kıdemli üyesidir; Bilim Akademisi tarafından BAGEP Genç Bilim İnsanı Ödülü'ne layık görülmüştür; 80'den fazla hakemli makale ve bildirisi bulunmaktadır.
3D SAR imaging: from sparse signal models to implicit neural representations
There is increasing interest in 3D reconstruction of objects from radar measurements. This interest is enabled by new data collection capabilities, in which airborne synthetic aperture radar (SAR) systems are able to interrogate a scene persistently and over a large range of aspect angles. Three-dimensional reconstruction is further motivated by an increasingly difficult class of surveillance and security challenges, including object detection and activity monitoring in urban scenes. The measurement process for SAR can be modeled as a Fourier operator with each pulse providing a 1D line segment from the 3D Spatial Fourier transform of the scene. Traditional Fourier imaging methods aggregate and index phase history data in the spatial Fourier domain collected through different apertures and apply an inverse 3D Fourier Transform. However, when inverse Fourier imaging is applied to sparsely sampled apertures, reconstruction quality is degraded due to the high sidelobes of the point spread function. In this talk we will start with a review of compressed sensing approaches that combine nonuniform fast Fourier transform methods with regularization priors for the scene such as sparsity, azimuthal persistence and vertical structures. Next, we will discuss different modeling approaches to anisotropic scattering from man-made objects to derive novel solutions to the 3D imaging problem through joint solution of the imaging problem over multiple neighboring views by exploiting the sparsity of dominant scattering centers in the scene and approximating the scattering coefficients using Gaussian basis functions in the azimuth domain. We will see that these sparse regularization methods are successful in producing point clouds consistent with the target shape. However the problem of extracting a surface representation from the point clouds remains a challenge. Recently, in computer vision, implicit deep neural representations like Neural Radiance Fields (NeRF) and its derivatives, become popular volume rendering methods. In the final part of the talk we will demonstrate how a NeRF like approach can be used to construct 3D surface representations from SAR data. We will illustrate the performance of each technique using measured and simulated datasets comprising of civilian vehicles.
Emre Ertin received his BS degrees in Electrical Engineering and Physics from the Bogazici University in 1992, and MSc Degree in Telecommunications and Signal Processing from the Imperial College, London, UK in 1993. In 1999, he received his PhD degree in Electrical Engineering from The Ohio State University. From 1999 to 2002, he was with the Core Technology Group at Battelle Memorial Institute. He joined the Department of Electrical and Computer Engineering of The Ohio State University in 2003, where he is currently an Associate Professor. At OSU, he served as principal investigator on NSF, AFOSR, AFRL, ARL, ARO, DARPA, IARPA and NIH funded projects on sensor signal processing, novel modalities and applications of sensor networks. He is leading one of the three research thrusts at the NIH mHealth Center for Discovery, Optimization and Translation of Temporally Precise Interventions (mDOT) aiming to develop methods, tools, and infrastructure necessary to pursue the discovery, optimization, and translation of next generation mobile Health interventions. He is the co-founder and director of technology for TegoSens, a technology startup developing radar based solutions for monitoring lung water content in congestive heart failure patients.
