Janusz Cichowski

Reversible video stream anonymization for video surveillance systems based on pixels relocation and watermarking

A method of reversible video image regions of interest anonymization for applications in video surveillance systems is described. A short introduction to the anonymization procedures is presented together with the explanation of its relation to visual surveillance. A short review of state of the art of sensitive data protection in media is included. An approach to reversible Region of Interest (ROI) hiding in video is presented, utilizing a new relocation algorithm for hashing and a watermarking technique for extra data embedding. Implemented application is described, and results obtained using it are reported. Future work and possible improvements to introduced algorithms are discussed.

Massive surveillance data processing with supercomputing cluster

We propose a new model of online surveillance data streams processing on a supercomputer.Massive parallelization enables online object detection in high-resolution video streams.Crowding detection in multi-camera image sequences is performed in nearly real-time.Online privacy protection in surveillance systems is possible on cluster architecture. In recent years, increasingly complex algorithms for automated analysis of surveillance data are being developed. The rapid growth in the number of monitoring installations and higher expectations of the quality parameters of the captured data result in an enormous computational cost of analyzing the massive volume of data. In this paper a new model of online processing of surveillance data streams is proposed, which assumes the use of services running within a supercomputer platform. The study presents some of the highly parallelized algorithms for detecting safety-threatening events in high-resolution-video streams, which were developed during the research, and discusses their performance on the supercomputer platform.

Analysis of Impact of Lossy Audio Compression on the Robustness of Watermark Embedded in the DWT Domain for Non-blind Copyright Protection

A methodology of non-blind watermarking of the audio content is proposed. The outline of audio copyright problem and motivation for practical applications are discussed. The algorithmic theory pertaining watermarking techniques is briefly introduced. The system architecture together with employed workflows for embedding and extracting the watermarks are described. The implemented approach is described and obtained results are reported. The possible attacks on the embedded watermark are described and the procedure of simulating the attacks is explained. The research is focused on the influence of lossy compression on the embedded watermark degradation. The peak signal to noise ratio and bit error rate are analyzed and compared. Advantages and disadvantages of the proposed approach are discussed. Future work and some possible improvements to the introduced methodology are explained.

Visual Data Encryption for Privacy Enhancement in Surveillance Systems

In this paper a methodology for employing reversible visual encryption of data is proposed. The developed algorithms are focused on privacy enhancement in distributed surveillance architectures. First, motivation of the study performed and a short review of preexisting methods of privacy enhancement are presented. The algorithmic background, system architecture along with a solution for anonymization of sensitive regions of interest are described. An analysis of efficiency of the developed encryption approach with respect to visual stream resolution and the number of protected objects is performed. Experimental procedures related to stream processing on a single core, single node and multiple nodes of the supercomputer platform are also provided. The obtained results are presented and discussed. Moreover, possible future improvements of the methodology are suggested.

Analysis of impact of audio modifications on the robustness of watermark for non-blind architecture

The aim of this paper is to assess the robustness of the non-blind audio content watermarking scheme proposed by the authors. The authors present the architecture of the designed system along with the employed workflows for embedding and extracting the watermark followed by the implementation phase description and the analysis of the experimental results. Some possible attack simulations on the embedded watermarks are reviewed, and the robustness of the proposed approach is evaluated in the context of the influence of lossy compression on the watermark degradation. Subjective and objective analyses are performed for the algorithm proposed by the authors and compared with the Audio Watermarking Tools (AWT) encoder. Finally, the advantages and drawbacks of the proposed approach are debated followed by the conclusion section outlining possible improvements to the proposed method.

Low-Level Music Feature Vectors Embedded as Watermarks

In this paper a method consisting in embedding low-level music feature vectors as watermarks into a musical signal is proposed. First, a review of some recent watermarking techniques and the main goals of development of digital watermarking research are provided. Then, a short overview of parameterization employed in the area of Music Information Retrieval is given. A methodology of non-blind watermarking applied to music-content description is presented. The system architecture for the embedding and recovery of the watermarks, along with the algorithms implemented, are described. The robustness of the watermark implemented is tested against audio file processing, such as re-sampling, filtration, time warping, cropping and lossy compression. Procedures for simulating musical signal alteration are explained with a focus on the influence of lossy compression on the degradation of the embedded watermark. The advantages and disadvantages of the proposed approach are discussed. An outline of future applications of the methodology introduced is also included.

Online sound restoration system for digital library applications

Audio signal processing algorithms were introduced to the new online non-commercial service for audio restoration intended to enhance the content of digitized audio repositories. Missing or distorted audio samples are predicted using neural networks and a specific implementation of the Jannsen interpolation method based on the autoregressive model (AR) combined with the iterative restoring of missing signal samples. Since the distortion prediction and compensations algorithms are computationally complex, an implementation which uses parallel computing has been proposed. Many archival recordings are at the same time clipped and affected by wideband noise. To restore those recordings, the algorithm based on the concatenation of signal clipping reduction and spectral expansion was proposed. The clipping reduction algorithm uses an intelligent interpolation to replace distorted samples with the predicted ones based on learning algorithms. Next, spectral expansion is performed in order to reduce the overall level of noise. The online service has been extended with some copyright protection mechanisms. Immunity of watermarks to the sound restoration is discussed with regards to low-level music feature vectors embedded as watermarks. Then, algorithmic issues pertaining watermarking techniques are briefly recalled. The architecture of the designed system is presented.

Further Developments of the Online Sound Restoration System for Digital Library Applications

New signal processing algorithms were introduced to the online service for audio restoration available at the web address: www.youarchive.net. Missing or distorted audio samples are estimated using a specific implementation of the Jannsen interpolation method. The algorithm is based on the autoregressive model (AR) combined with the iterative complementation of signal samples. Since the interpolation algorithm is computationally complex, an implementation which uses parallel computing has been proposed. Many archival and homemade recordings are at the same time clipped and contain wideband noise. To restore those recordings, the algorithm based on the concatenation of signal clipping reduction and spectral expansion was proposed. The clipping reduction algorithm uses interpolation to replace distorted samples with the estimated ones. Next, spectral expansion is performed in order to reduce the overall level of noise. The online service has been extended also with some copyright protection mechanisms. Certain issues related to the audio copyright problem are discussed with regards to low-level music feature vectors embedded as watermarks. Then, algorithmic issues pertaining watermarking techniques are briefly recalled. The architecture of the designed system along with the employed workflow for embedding and extracting the watermark are described. The implementation phase is presented and the experimental results are reported. The chapter is concluded with a presentation of experimental results of application of described algorithmic extensions to the online sound restoration service.