High capacity data embedding schemes for digital media

High capacity image data hiding methods and robust high capacity digital audio watermarking algorithms are studied in this thesis. The main results of this work are the development of novel algorithms with state-of-the-art performance, high capacity and transparency for image data hiding and robustness, high capacity and low distortion for audio watermarking.En esta tesis se estudian y proponen diversos métodos de data hiding de imágenes y watermarking de audio de alta capacidad. Los principales resultados de este trabajo consisten en la publicación de varios algoritmos novedosos con rendimiento a la altura de los mejores métodos del estado del arte, alta capacidad y transparencia, en el caso de data hiding de imágenes, y robustez, alta capacidad y baja distorsión para el watermarking de audio.En aquesta tesi s'estudien i es proposen diversos mètodes de data hiding d'imatges i watermarking d'àudio d'alta capacitat. Els resultats principals d'aquest treball consisteixen en la publicació de diversos algorismes nous amb rendiment a l'alçada dels millors mètodes de l'estat de l'art, alta capacitat i transparència, en el cas de data hiding d'imatges, i robustesa, alta capacitat i baixa distorsió per al watermarking d'àudio.Societat de la informació i el coneixemen

High capacity audio watermarking using FFT amplitude interpolation

An audio watermarking technique in the frequency domain which takes advantage of interpolation is proposed. Interpolated FFT samples are used to generate imperceptible marks. The experimental results show that the suggested method has very high capacity (about 3kbps), without significant perceptual distortion (ODG about -0.5) and provides robustness against common audio signal processing such as echo, add noise, filtering, resampling and MPEG compression (MP3). Depending on the specific application, the tuning parameters could be selected adaptively to achieve even more capacity and better transparency

Robust high-capacity audio watermarking based on FFT amplitude modification

This paper proposes a novel robust audio watermarking algorithm to embed data and extract it in a bit-exact manner based on changing the magnitudes of the FFT spectrum. The key point is selecting a frequency band for embedding based on the comparison between the original and the MP3 compressed/decompressed signal and on a suitable scaling factor. The experimental results show that the method has a very high capacity (about 5 kbps), without significant perceptual distortion (ODG about -0.25) and provides robustness against common audio signal processing such as added noise, filtering and MPEG compression (MP3). Furthermore, the proposed method has a larger capacity (number of embedded bits to number of host bits rate) than recent image data hiding methods

Reversible image data hiding based on gradient adjusted prediction

The present study illustrates a new lossless data hiding method for digital images using image prediction technique. In the proposed method which is based on gradient-adjusted prediction (GAP), first prediction errors are computed and then the error values are slightly modified through shifting the prediction errors. The modified errors are used for embedding the data. Experimental results of present research have demonstrated that the proposed method called shifted gradient-adjusted prediction error (SGAPE) is capable of hiding more secret data with absolutely high PSNR

Secure logarithmic audio watermarking scheme based on the human auditory system

This paper proposes a high capacity audio watermarking algorithm in the logarithm domain based on the absolute threshold of hearing (ATH) of the human auditory system (HAS) which makes this scheme a novel technique. Considering the fact that the human ear requires more precise samples at low amplitudes (soft sounds), the use of the logarithm helps us design a logarithmic quantization algorithm. The key idea is to divide the selected frequency band into short frames and quantize the samples based on the HAS. Using frames and the HAS improves the robustness, since embedding a secret bit into a set of samples is more reliable than embedding it into a single sample. In addition, the quantization level is adjusted according to the HAS. Apart from remarkable capacity, transparency and robustness, this scheme provides three parameters (frequency band, scale factor and frame size) which facilitate the regulation of the watermarking properties. The experimental results show that the method has a high capacity (800 to 7000 bits per second), without significant perceptual distortion (ODG is greater than ¿1) and provides robustness against common audio signal processing such as added noise, filtering and MPEG compression (MP3)

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewedThis paper proposes a novel high capacity audio watermarking algorithm to embed data and extract them in a bit-exact manner by changing some of the magnitudes of the FFT spectrum. The key idea is to divide the FFT spectrum into short frames and change the magnitudes of the selected FFT samples using linear regression and the average of the samples of each frame. Using the average of FFT magnitudes leads to improved robustness, since this variable is more invariant against manipulations compared to the magnitudes of single samples. In addition, linear regression helps to minimize the alterations of FFT samples, which results in better transparency. Apart from very remarkable capacity, transparency and robustness, this scheme provides three parameters which facilitate the regulation of these properties. The experimental results show that the method has a high capacity (0.5 to 2.3 kbps), without significant perceptual distortion (ODG is about ¿1) and provides robustness against common audio signal processing such as echo, added noise, filtering and MPEG compression (MP3)

Subjectively adapted high capacity lossless image data hiding based on prediction errors

This article reports on a lossless data hiding scheme for digital images where the data hiding capacity is either determined by minimum acceptable subjective quality or by the demanded capacity. In the proposed method data is hidden within the image prediction errors, where the most well-known prediction algorithms such as the median edge detector (MED), gradient adjacent prediction (GAP) and Jiang prediction are tested for this purpose. In this method, first the histogram of the prediction errors of images are computed and then based on the required capacity or desired image quality, the prediction error values of frequencies larger than this capacity are shifted. The empty space created by such a shift is used for embedding the data. Experimental results show distinct superiority of the image prediction error histogram over the conventional image histogram itself, due to much narrower spectrum of the former over the latter. We have also devised an adaptive method for hiding data, where subjective quality is traded for data hiding capacity. Here the positive and negative error values are chosen such that the sum of their frequencies on the histogram is just above the given capacity or above a certain quality

Lossless Image Data Embedding in Plain Areas

This letter presents a lossless data hiding scheme for digital images which uses an edge detector to locate plain areas for embedding. The proposed method takes advantage of the well-known gradient adjacent prediction utilized in image coding. In the suggested scheme, prediction errors and edge values are first computed and then, excluding the edge pixels, prediction error values are slightly modified through shifting the prediction errors to embed data. The aim of proposed scheme is to decrease the amount of modified pixels to improve transparency by keeping edge pixel values of the image. The experimental results have demonstrated that the proposed method is capable of hiding more secret data than the known techniques at the same PSNR, thus proving that using edge detector to locate plain areas for lossless data embedding can enhance the performance in terms of data embedding rate versus the PSNR of marked images with respect to original image.Este artículo presenta un esquema de la pérdida de datos para las imágenes digitales que utilizan un detector de bordes para localizar las zonas llanas de incrustación. El método propuesto se aprovecha de la predicción del gradiente adyacente conocido utilizado en la codificación de la imagen. En el esquema propuesto, se calculan primero los errores de predicción y los valores límite y luego, con exclusión de los píxeles del borde, se modifican ligeramente los valores de error de predicción a través del cambio de los errores de predicción para integrar los datos. El objetivo del programa propuesto es reducir la cantidad de píxeles modificados para mejorar la transparencia, manteniendo los valores de borde del píxel de la imagen. Los resultados experimentales han demostrado que el método propuesto es capaz de ocultar los datos más secretos de las técnicas conocidas en el mismo PSNR, lo que demuestra que el uso del detector de bordes para localizar las zonas llanas de la incrustación de datos sin pérdida puede mejorar el rendimiento en términos de velocidad de incrustación de datos en comparación con el PSNR de las imágenes marcadas con respecto a la imagen original.Aquest article presenta un esquema sobre la pèrdua de dades per les imatges digitals que utilitzen un detector de vores per localitzar les zones planes d'incrustació. El mètode proposat s¿aprofita de la predicció del gradient adjacent conegut i utilitzat en la codificació de la imatge. A l'esquema proposat es calculen primer els errors de predicció i els valors límit i, després, excloent els píxels de les vores, es modifiquen lleugerament els valors d'error de predicció a través del canvi dels errors de predicció per integrar les dades. L'objectiu del programa proposat és reduir la quantitat de píxels modificats per millorar la transferència, mantenint els valors de la vora del píxel de la imatge. Els resultats experimentals han demostrat que el mètode proposat és capaç d'amagar les dades més secretes de les tècniques conegudes al PSNR, fet que demostra que l'ús del detector de vores per localitzar les zones planes de la incrustació de dades sense pèrdua pot millorar el rendiment en termes de velocitat d'incrustació de dades en comparació amb el PSNR de les imatges marcades respecte a la imatge original