This paper deals with the design of a practical self-recovery mechanism for lossy compressed JPEG images. We extend a recently proposed model of the content reconstruction problem based on digital fountain codes to take into account the impact of emerging watermark extraction and block classification errors. In contrast to existing methods, our scheme guarantees a high and stable level of reconstruction quality. Instead of introducing reconstruction artifacts, emerging watermark extraction errors penalize the achievable tampering rates. We introduce new mechanisms that allow for handling high-resolution and color images efficiently. In order to analyze the behavior of our scheme, we derive an improved model to calculate the reconstruction success probability. We introduce a new hybrid mechanism for spreading the reference information over the entire image, which allows to find a good balance between the achievable tampering rates and the computational complexity. Such an approach reduced the watermark embedding time from the order of several minutes to the order of single seconds, even on mobile devices.

Annotation watermarking is a technique that allows to associate content descriptions with digital images in a persistent and format independent manner. It is commonly used in medical applications and, hence, existing schemes have been designed to meet rigorous watermark transparency requirements. As a result, the effective capacity of such schemes is severely limited. In this paper, we present a new approach to annotation watermarking. We adopt the fountain coding paradigm and design a convenient watermark communication architecture which resembles a traditional packet network. Our approach allows for straightforward incorporation of content adaptivity, robustness against cropping and support for multiple data streams. In our study, we focus on high-capacity annotations and we assume different requirements with respect to the fidelity of the watermarked images. Our scheme is robust against lossy JPEG compression and cropping. This paper describes the principles of the proposed approach and presents the results of it’s experimental evaluation.

Intelligent monitoring is currently one of the most prominent research areas. Numerous aspects of such schemes need to be addressed by implementation of various modules covering a wide range of algorithms, beginning from video analytic modules, through quality assessment, up to integrity verification. The goal of this paper is to provide a brief overview of the most recent research results regarding various aspects of the video surveillance processing chain. Specifically, the paper describes a scheme for automatic recognition of the make and model of passing vehicles, the state-of-the-art in quality assessment for recognition tasks, and a system for verification of digital evidence integrity. Concluding remarks highlight the perspectives for further development of the described techniques, and the related research directions.