Prof. Bruno Carpentieri  
University of Salerno, Italy

Speech Title: Understanding Data Compression: Acquired Knowledge and Practices
Abstract: Digital data compression has become a central topic in modern information technology. Without it, key innovations such as digital television, mobile communications, and broader digital data transmission would not be feasible. Compression is closely intertwined with clustering and learning, each representing dimensions of the same multifaceted problem. Interestingly, the insights gained from compression processes can inform both learning algorithms and clustering techniques. In this presentation, we will explore recent advancements in data compression and examine its deep connections with learning and clustering methodologies.

Biography: Bruno Carpentieri graduated in Computer Science at the University of Salerno, and then obtained the Master of Arts Degree and the Philosopy Doctorate Degree in Computer Science at the Brandeis University (Waltham, MA, USA).

Since 1991, he was first Researcher, then Associate Professor and finally Full Professor of Computer Science at the University of Salerno (Italy).

His research interests include data compression and information hiding.

He was Associate Editor of IEEE Trans magazine. on Image Processing and is still Associate Editor of the international journals Algorithms and Security and Communication Networks. He was also chair and organizer of various international conferences including the International Conference on Data Compression, Communication and Processing, co-chair of the International Conference on Compression and Complexity of Sequences, and, for many years, a member of the program committee of the IEEE Data Compression Conference.

He has been responsible for several European Commission contracts in the field of data compression (compression of digital images and videos).

He directs the Data Compression Laboratory at the Computer Science Department of the University of Salerno.

 

 

Assoc. Prof. Akbar Sheikh-Akbari  
Leeds Beckett University, UK

Speech Title: From Pixels to Proof: Forensic Techniques for Source Camera Identification
Abstract: The successful investigation and prosecution of high-stakes crimes—ranging from child exploitation and insurance fraud to movie piracy and scientific misconduct—hinge critically on the availability of irrefutable digital evidence. When such evidence includes images or videos, establishing the precise source device becomes paramount. Over the past decade, significant research has focused on image and video source camera identification, employing both hardware-based artifacts (e.g., sensor pattern noise, lens distortion) and software-based traces (e.g., colour filter array, auto white balance). This talk provides a comprehensive overview of these techniques, categorizing them into brand/model-level identification and known device matching. It critically evaluates their effectiveness, highlighting strengths, limitations, and the evolving challenges in ensuring forensic reliability in digital media attribution.

Biography: Dr Akbar Sheikh-Akbari is an associate professor in School of Built Environment, Engineering and Computing. He holds a BSc (Hons), MSc (Distinction), and PhD in Electronic and Electrical Engineering. Dr. Sheikh-Akbari began his academic career as a postdoctoral researcher at Bristol University, working on an EPSRC project in stereo/multi-view video processing. Transitioning to industry, he specialized in real-time embedded video analytics systems.
In 2015, Dr. Sheikh-Akbari joined Leeds Beckett University as a Senior Lecturer. He has successfully completed several Knowledge Transfer Partnership (KTP) projects, including the application of RFIDs for asset management in greeting cards and developing a scalable system for monitoring and analysing behavioural patterns with Omega Security Systems, both graded OUTSTANDING by Innovate UK. He is currently leading a KTP project on developing novel hyper-spectral imaging capabilities to screen for aflatoxins in pistachios.
Dr. Sheikh-Akbari has supervised 12 PhD projects to completion and is currently overseeing 6 PhD projects. He has published over 140 conference and journal papers. His research interests include hyperspectral image processing, image source camera identification, biometric identification techniques (iris, ear, and face recognition), color constancy adjustment techniques, standard and non-standard image/video codecs, image resolution enhancement, multi-view image/video processing, video analytics, and edge detection in low SNR environments.