LSB steganography detection in monochromatic still images using artificial neural networks

Julián D. Miranda, Diego J. Parada

    Producción científica: Contribución a una revistaArtículo en revista científica indexadarevisión exhaustiva

    5 Citas (Scopus)

    Resumen

    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.

    Idioma originalInglés
    Páginas (desde-hasta)785-805
    Número de páginas21
    PublicaciónMultimedia Tools and Applications
    Volumen81
    N.º1
    DOI
    EstadoPublicada - ene. 2022

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