2023 Chairs’ Welcome

Welcome to the 11th edition of the ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec ‘23). This year’s workshop continues the tradition of representing one of the prime events in information hiding and multimedia security, attracting researchers and practitioners worldwide. Carrying on with the efforts of the previous edition to overcome the Pandemics and reunite the IH&MMSec community to present and discuss their work, this year’s meeting is held fully in person at the Water Tower Campus of Loyola University Chicago, located right at the heart of the Windy City. Bathed by the fresh waters of Lake Michigan, Chicago is the 3rd largest city in the USA, with a strong, multiethnic, and multicultural community. Besides fostering Science and their research during the workshop, attendees have countless opportunities for outdoor citywise activities, blessed by the beginning of the American Midwest Summer

A Computer Vision Method for Estimating Velocity from Jumps

Athletes routinely undergo fitness evaluations to evaluate their training progress. Typically, these evaluations require a trained professional who utilizes specialized equipment like force plates. For the assessment, athletes perform drop and squat jumps, and key variables are measured, e.g. velocity, flight time, and time to stabilization, to name a few. However, amateur athletes may not have access to professionals or equipment that can provide these assessments. Here, we investigate the feasibility of estimating key variables using video recordings. We focus on jump velocity as a starting point because it is highly correlated with other key variables and is important for determining posture and lower-limb capacity. We find that velocity can be estimated with a high degree of precision across a range of athletes, with an average R-value of 0.71 (SD = 0.06)

Spotting the difference: Context retrieval and analysis for improved forgery detection and localization

As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place. In this vein, we introduce five new strongly invariant image comparison methods and test their effectiveness under heavy noise, rotation, and color space changes. Lastly, we show the effectiveness of these methods compared to passive image forensics using Nimble [1], a new, state-of-the-art dataset from the National Institute of Standards and Technology (NIST)