Audio steganalysis has been little explored due to its complexity and randomness, which complicate the analysis. Audio files generate marks in the frequency domain; these marks are known as fingerprints and make the files unique. This allows us to differentiate between audio vectors. In this work, the use of the Goertzel algorithm as a steganalyzer in the frequency domain is combined with the proposed sliding window adaptation to allow the analyzed audio vectors to be compared, enabling the differences between the vectors to be identified. We then apply linear prediction to the vectors to detect any modifications in the acoustic signatures. The implemented Goertzel algorithm is computationally less complex than other proposed stegoanalyzers based on convolutional neural networks or other types of classifiers of lower complexity, such as support vector machines (SVD). These methods previously required an extensive audio database to train the network, and thus detect possible stegoaudio through the matches they find. Unlike the proposed Goertzel algorithm, which works individually with the audio vector in question, it locates the difference in tone and generates an alert for the possible stegoaudio. In this work, we apply the classic Goertzel algorithm to detect frequencies that have possibly been modified by insertions or alterations of the audio vectors. The final vectors are plotted to visualize the alteration zones. The obtained results are evaluated qualitatively and quantitatively. To perform a double check of the fingerprint of the audio vectors, we obtain a linear prediction error to establish the percentage of statistical dependence between the processed audio signals. To validate the proposed method, we evaluate the audio quality metrics (AQMs) of the obtained result. Finally, we implement the stegoanalyzer oriented to AQMs to corroborate the obtained results. From the results obtained for the performance of the proposed stegoanalyzer, we demonstrate that we have a success rate of 100%.

There are challenges in the image-based research to obtain information from the objects in the scene. Moreover, an image is a set of data points that can be processed as an object in similarity way. In addition, the research fields can be merged to generate a method for information extraction and pixel classification. A complete method is proposed to extract information from the data and generate a classification model capable to isolate those pixels that are plant from others are not. Some quantitative and qualitative results are shown to compare methods to extract information and create the best model. Classical and threshold-based state-of-art methods are grouped in the present work for reference and application in image segmentation, obtaining acceptable results in the plant isolation.

A binary automatic segmentation algorithm for digital images of whales is presented. The algorithm is established by two cascade blocks in order to process an image and classify its pixels. The first block preprocess the information with a 1% grid subsampling over all pixels in each image, those samples are used to generate new indexes based in the RGB data channels which represents the colors of the whale. Because the ratio of a pixel whose is detected as whale and clutter does not tend to 1:1, an equal number random sampling selection is made in order to work with a homogeneous distribution in training process. An inference engine is the second block, this classify those pixels that are detected as whale according to its colorimetric features and samples obtained in the previous block. This block is realized by Principal Component Analysis (PCA), reducing the feature number for the model and increasing the data separability, and Logistic Regression for pixel classification. Our approach avoids the requirement of extensive computing power and specialized equipment, this allow the algorithm portability and implementation in consumer devices. The algorithm needs an annotated dataset because it is based in supervised learning strategy. In addition, this algorithm minimizes the required time to develop an automatic segmentation system around any object of interest in images.