Andrzej Dziech

Multimedia Communications, Services and Security

An intelligent surveillance system based on visual information gathered by smart cameras, aimed at traffic monitoring with emphasis on traffic events caused by cars, is presented in the paper. The system components and their capabilities for automatic detection and recognition of selected parameters of cars, as well as different aspects of system efficiency, are described and discussed in detail. Smart facilities for Make and Model Recognition (MMR), License Plate Recognition (LPR) and Color Recognition (CR), embedded in the system in the form of their individual software implementations, are analyzed and their recognition rates detailed. Finally, a discussion of the systems efficiency as a whole, with an insight into possible future improvements, is included in the conclusion.

Efficient Method for Content Reconstruction With Self-Embedding

This paper presents a new model of the content reconstruction problem in self-embedding systems, based on an erasure communication channel. We explain why such a model is a good fit for this problem, and how it can be practically implemented with the use of digital fountain codes. The proposed method is based on an alternative approach to spreading the reference information over the whole image, which has recently been shown to be of critical importance in the application at hand. Our paper presents a theoretical analysis of the inherent restoration trade-offs. We analytically derive formulas for the reconstruction success bounds, and validate them experimentally with Monte Carlo simulations and a reference image authentication system. We perform an exhaustive reconstruction quality assessment, where the presented reference scheme is compared to five state-of-the-art alternatives in a common evaluation scenario. Our paper leads to important insights on how self-embedding schemes should be constructed to achieve optimal performance. The reference authentication system designed according to the presented principles allows for high-quality reconstruction, regardless of the amount of the tampered content. The average reconstruction quality, measured on 10000 natural images is 37 dB, and is achievable even when 50% of the image area becomes tampered.

Visual detection of knives in security applications using Active Appearance Models

In this paper, a novel application of Active Appearance Models to detecting knives in images is presented. In contrast to its popular applications in face segmentation and medical image analysis, we not only use this computer vision algorithm to locate an object that is known to exist in an analysed image, but–using an interest point typical of knives–also try to identify whether or not a knife exists in the image in question. We propose an entire detection scheme and examine its performance on a sample test set. The work presented in this paper aims to create a robust visual knife-detector to be used in security applications.

Towards Practical Self-Embedding for JPEG-Compressed Digital Images

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.

Adaptive Self-Embedding Scheme With Controlled Reconstruction Performance

In this paper, we address the problem of adaptive self-embedding, where the reconstruction quality is controlled individually for different fragments of a digital image. We focus on the impact of incorporating content adaptivity features on the restoration success conditions and the achievable reconstruction performance. We analyze the problem theoretically and validate the obtained results experimentally with a fully functional selfembedding scheme. Our analysis shows that introduction of multiple reconstruction profiles, even with significantly lower restoration fidelity, does not need to improve the achievable tampering rate bounds. The obtained fine-grained control over the reconstruction process is exploited to provide guarantees on certain performance aspects. Based on the derived theoretical model, we propose a procedure for optimization of the overall reconstruction quality given constraints on the desired target tampering rate and the required quality level for selected image fragments. Such guarantees are of principal importance in a number of applications.

A new approach to high-capacity annotation watermarking based on digital fountain codes

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.

Towards Robust Visual Knife Detection in Images: Active Appearance Models Initialised with Shape-Specific Interest Points

In this paper a novel application of Active Appearance Models to detecting knives in images is presented. Contrary to popular applications of this computer vision algorithm such as face segmentation or medical image analysis, we use it not only to locate an instance of an object that is known to exist in the analysed image. Using an interest point typical to knives we try to answer the question, whether a knife is or is non-existent in the image in question. We propose an entire detection scheme and examine its performance on a sample test set. The work presented in this paper aims at creating a robust visual knife detector that will find application in computerised monitoring of the public using CCTV.

A scheme for censorship of sensitive image content with high-quality reconstruction ability

Multimedia files often contain fragments with sensitive content that should not be visible to everyone. Such content is usually censored prior to distribution. We propose a technique that allows to blur selected fragments of the image while retaining details necessary for original appearance reconstruction in the image itself. The information is retained by means of a digital watermark. The protected image can be viewed in ordinary viewers and a dedicated decoder is required for content restoration. The decoder ensures that only authorized recipients are able to see the protected fragments. We evaluate the proposed approach in terms of protection capacity and reconstruction quality also under lossy compression. The results show that high image quality is achievable and the proposed scheme might be beneficial for certain applications. Moderate complexity of the decoding process makes it feasible for real-time usage scenarios.

Key indicators for monitoring of audiovisual quality

Automating multimedia quality checking is currently based on finding major audio/video artifacts. The “Monitoring Of Audio-Visual quality by key Indicators” (MOAVI) subgroup of the Video Quality Experts Group is an open collaborative project for developing No-Reference models for monitoring quality. MOAVI is a complementary, industry-driven alternative to Quality of Experience used to automatically measure quality by using simple indicators of perceived degradation. The goal is to develop a set of key indicators describing quality in general, and to select subsets for each potential application. Therefore, the MOAVI project concentrates on models based on key indicators contrary to models predicting overall quality.

A novel approach to adaptive image authentication

In this paper we address the issue of the trade-off between the tampering rate and the reconstruction quality of image authentication systems. We adopt the fountain coding paradigm and design an adaptive content reconstruction scheme. The scheme conforms the reconstruction quality of individual image fragments both to the local texture properties and to the specified requirements. Experimental evaluation confirms that a framework based on this approach is a valid and convenient model of the performance of the considered reconstruction problem.