Embedding graphic content in multimedia through steganography is a useful and fast practice to hide information. However, detecting the use of this technique is complex and sometimes unsuccessful because variations are not visually perceptible. This article proposes the use of a binary classification model based on artificial neural networks to detect the presence of LSB steganography on monochromatic still images of 256x256 and 8 bits, based on the Standford Genome Project. The steganograms were generated by varying the payload from 0.1 to 0.5 to obtain image pairs of carriers and steganograms. For each steganogram, the following features were extracted from image histograms: kurtosis, skewness, standard deviation, range, median, harmonic mean, Hjorth mobility, and complexity. The results show that the classifier reaches a 91.45% accuracy in detecting LSB steganography when learning from all payloads, as well as a 96.78% individual classification accuracy in the best case with a payload of 0.5.

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This paper presents an integrated model for seismic events detection in Colombia using machine learning techniques. Machine learning is used to identify P-wave windows in historic records and hence detect seismic events. The proposed model has five modules that group the basic detection system procedures: the seeking, gathering, and storage seismic data module, the reading of seismic records module, the analysis of seismological stations module, the sample selection module, and the classification process module. An explanation of each module is given in conjunction with practical recommendations for its implementation. The resulting model allows understanding the integration of the phases required for the design and development of an offline seismic event detection system.