Perceptual Video Hashing for Content Identification and Authentication

Perceptual hashing has been broadly used in the literature to identify similar contents for video copy detection. It has also been adopted to detect malicious manipulations for video authentication. However, targeting both applications with a single system using the same hash would be highly desirable as this saves the storage space and reduces the computational complexity. This paper proposes a perceptual video hashing system for content identification and authentication. The objective is to design a hash extraction technique that can withstand signal processing operations on one hand and detect malicious attacks on the other hand. The proposed system relies on a new signal calibration technique for extracting the hash using the discrete cosine transform (DCT) and the discrete sine transform (DST). This consists of determining the number of samples, called the normalizing shift, that is required for shifting a digital signal so that the shifted version matches a certain pattern according to DCT/DST coefficients. The rationale for the calibration idea is that the normalizing shift resists signal processing operations while it exhibits sensitivity to local tampering (i.e., replacing a small portion of the signal with a different one). While the same hash serves both applications, two different similarity measures have been proposed for video identification and authentication, respectively. Through intensive experiments with various types of video distortions and manipulations, the proposed system has been shown to outperform related state-of-the art video hashing techniques in terms of identification and authentication with the advantageous ability to locate tampered regions

How Is a Moving Target Continuously Tracked Behind Occluding Cover?

Office of Naval Research (N00014-95-1-0657, N00014-95-1-0409

Automatic removal of music tracks from tv programmes

This work pertains to in the research area of sound source separation. It deals with the problem of automatically removing musical segments from TV programmes. The dissertation proposes the utilisation of a pre-existant music recording, easily obtainable from o cially published CDs related to the audiovisual piece, as a reference for the undesired signal. The method is able to automatically detect small segments of the speci c musictrack spread among the whole audio signal of the programme, even if they appear with time-variable gain, or after having su ered linear distortions, such as being processed by equalization lters, or non-linear distortions, such as dynamic range compression. The project developed a quick-search algorithm using audio ngerprint techniques and hash-token data types to lower the algorithm complexity. The work also proposes the utilisation of a Wiener ltering technique to estimate potential equalization lter coe cients and uses a template matching algorithm to estimate time-variable gains to properly scale the musical segments to the correct amplitude they appear in the mixture. The key components of the separation system are presented, and a detailed description of all the algorithms involved is reported. Simulations with arti cial and real TV programme soundtracks are analysed and considerations about new future works are made. Furthermore, given the unique nature of this project, it is possible to say the dissertation is pioneer in the subject, becoming an ideal source of reference for other researchers that want to work in the area.Este trabalho está inserido na área de pesquisa de separação de fontes sonoras. Ele trata do problema de remover automaticamente segmentos de música de programas de TV. A tese propõe a utilização de uma gravação musical pré-existente, facilmente obtida em CDs oficialmente publicados relacionados à obra audiovisual, como referência para o sinal não desejado. O método é capaz de detectar automaticamente pequenos segmentos de uma trilha musical específica espalhados pelo sinal de áudio do programa, mesmo que eles apareçam com um ganho variante no tempo, ou tenham sofrido distorções lineares, como processamento por filtros equalizadores, ou distorções não lineares, como compressão de sua faixa dinâmica. O projeto desenvolveu um algoritmo de busca rápida usando técnicas de impressão digital de áudio e dados do tipo hash-token para diminuir a complexidade. O trabalho também propõe a utilização da técnica de filtragem de Wiener para estimar os coe cientes de um potencial filtro de equalização, e usa um algoritmo de template matching para estimar ganhos variantes no tempo para escalar corretamente os excertos musicais até a amplitude correta com que eles aparecem na mistura. Os componentes-chaves para o sistema de separação são apresentados, e uma descrição detalhada de todos os algoritmos envolvidos é reportada. Simulações com trilhas sonoras artificiais e de programas de TV reais são analisadas e considerações sobre novos trabalhos futuros são feitas. Além disso, dada a natureza única do projeto, é possível dizer que a dissertação é pioneira no assunto, tornando-se uma fonte de referência para outros pesquisadores que queiram trabalhar na área

Combined robust and fragile watermarking algorithms for still images. Design and evaluation of combined blind discrete wavelet transform-based robust watermarking algorithms for copyright protection using mobile phone numbers and fragile watermarking algorithms for content authentication of digital still images using hash functions.

This thesis deals with copyright protection and content authentication for still images. New blind transform domain block based algorithms using one-level and two-level Discrete Wavelet Transform (DWT) were developed for copyright protection. The mobile number with international code is used as the watermarking data. The robust algorithms used the Low-Low frequency coefficients of the DWT to embed the watermarking information. The watermarking information is embedded in the green channel of the RGB colour image and Y channel of the YCbCr images. The watermarking information is scrambled by using a secret key to increase the security of the algorithms. Due to the small size of the watermarking information comparing to the host image size, the embedding process is repeated several times which resulted in increasing the robustness of the algorithms. Shuffling process is implemented during the multi embedding process in order to avoid spatial correlation between the host image and the watermarking information. The effects of using one-level and two-level of DWT on the robustness and image quality have been studied. The Peak Signal to Noise Ratio (PSNR), the Structural Similarity Index Measure (SSIM) and Normalized Correlation Coefficient (NCC) are used to evaluate the fidelity of the images. Several grey and still colour images are used to test the new robust algorithms. The new algorithms offered better results in the robustness against different attacks such as JPEG compression, scaling, salt and pepper noise, Gaussian noise, filters and other image processing compared to DCT based algorithms. The authenticity of the images were assessed by using a fragile watermarking algorithm by using hash function (MD5) as watermarking information embedded in the spatial domain. The new algorithm showed high sensitivity against any tampering on the watermarked images. The combined fragile and robust watermarking caused minimal distortion to the images. The combined scheme achieved both the copyright protection and content authentication

Map-Based Localization for Unmanned Aerial Vehicle Navigation

Unmanned Aerial Vehicles (UAVs) require precise pose estimation when navigating in indoor and GNSS-denied / GNSS-degraded outdoor environments. The possibility of crashing in these environments is high, as spaces are confined, with many moving obstacles. There are many solutions for localization in GNSS-denied environments, and many different technologies are used. Common solutions involve setting up or using existing infrastructure, such as beacons, Wi-Fi, or surveyed targets. These solutions were avoided because the cost should be proportional to the number of users, not the coverage area. Heavy and expensive sensors, for example a high-end IMU, were also avoided. Given these requirements, a camera-based localization solution was selected for the sensor pose estimation. Several camera-based localization approaches were investigated. Map-based localization methods were shown to be the most efficient because they close loops using a pre-existing map, thus the amount of data and the amount of time spent collecting data are reduced as there is no need to re-observe the same areas multiple times. This dissertation proposes a solution to address the task of fully localizing a monocular camera onboard a UAV with respect to a known environment (i.e., it is assumed that a 3D model of the environment is available) for the purpose of navigation for UAVs in structured environments. Incremental map-based localization involves tracking a map through an image sequence. When the map is a 3D model, this task is referred to as model-based tracking. A by-product of the tracker is the relative 3D pose (position and orientation) between the camera and the object being tracked. State-of-the-art solutions advocate that tracking geometry is more robust than tracking image texture because edges are more invariant to changes in object appearance and lighting. However, model-based trackers have been limited to tracking small simple objects in small environments. An assessment was performed in tracking larger, more complex building models, in larger environments. A state-of-the art model-based tracker called ViSP (Visual Servoing Platform) was applied in tracking outdoor and indoor buildings using a UAVs low-cost camera. The assessment revealed weaknesses at large scales. Specifically, ViSP failed when tracking was lost, and needed to be manually re-initialized. Failure occurred when there was a lack of model features in the cameras field of view, and because of rapid camera motion. Experiments revealed that ViSP achieved positional accuracies similar to single point positioning solutions obtained from single-frequency (L1) GPS observations standard deviations around 10 metres. These errors were considered to be large, considering the geometric accuracy of the 3D model used in the experiments was 10 to 40 cm. The first contribution of this dissertation proposes to increase the performance of the localization system by combining ViSP with map-building incremental localization, also referred to as simultaneous localization and mapping (SLAM). Experimental results in both indoor and outdoor environments show sub-metre positional accuracies were achieved, while reducing the number of tracking losses throughout the image sequence. It is shown that by integrating model-based tracking with SLAM, not only does SLAM improve model tracking performance, but the model-based tracker alleviates the computational expense of SLAMs loop closing procedure to improve runtime performance. Experiments also revealed that ViSP was unable to handle occlusions when a complete 3D building model was used, resulting in large errors in its pose estimates. The second contribution of this dissertation is a novel map-based incremental localization algorithm that improves tracking performance, and increases pose estimation accuracies from ViSP. The novelty of this algorithm is the implementation of an efficient matching process that identifies corresponding linear features from the UAVs RGB image data and a large, complex, and untextured 3D model. The proposed model-based tracker improved positional accuracies from 10 m (obtained with ViSP) to 46 cm in outdoor environments, and improved from an unattainable result using VISP to 2 cm positional accuracies in large indoor environments. The main disadvantage of any incremental algorithm is that it requires the camera pose of the first frame. Initialization is often a manual process. The third contribution of this dissertation is a map-based absolute localization algorithm that automatically estimates the camera pose when no prior pose information is available. The method benefits from vertical line matching to accomplish a registration procedure of the reference model views with a set of initial input images via geometric hashing. Results demonstrate that sub-metre positional accuracies were achieved and a proposed enhancement of conventional geometric hashing produced more correct matches - 75% of the correct matches were identified, compared to 11%. Further the number of incorrect matches was reduced by 80%

A robust multi-watermarking algorithm for medical images based on DTCWT-DCT and Henon map

To resolve the contradiction between existing watermarking methods—which are not compatible with the watermark’s ability to resist geometric attacks—and robustness, a robust multi-watermarking algorithm suitable for medical images is proposed. First, the visual feature vector of the medical image was obtained by dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) to perform multi-watermark embedding and extraction. Then, the multi-watermark was pre-processed using the Henon map chaotic encryption technology to strengthen the security of watermark information, and combined with the concept of zero watermark to make the watermark able to resist both conventional and geometric attacks. Experimental results show that the proposed algorithm can effectively extract watermark information; it implements zero watermarking and blind extraction. Compared with existing watermark technology, it has good performance in terms of its robustness and resistance to geometric attacks and conventional attacks, especially in geometric attacks