Deep learning

Pattern recognition (psychology)

JPEG

Discrete cosine transform

Bandwidth (computing)

Feature (linguistics)

Computer network

Steganalysis

Image (mathematics)

Steganography

Distortion (music)

Philosophy

Artificial intelligence

Amplifier

Gabor filter

Computer science

Linguistics

Feature extraction

Computer vision

Embedding

Ensemble learning

Electronics

In this article, the problem of detecting JPEG images, which have been steganographically manipulated, is discussed. The performance of employing various shallow and deep learning algorithms in image steganography detection is analyzed. The data, images from the BOSS database, were used with information hidden using three popular steganographic algorithms: JPEG universal wavelet relative distortion (J-Uniward), nsF5, and uniform embedding revisited distortion (UERD) at two density levels. Various feature spaces were verified, with the discrete cosine transform residuals (DCTR) and the Gabor filter residuals (GFR) yielding best results. Almost perfect detection was achieved for the nsF5 algorithm at 0.4 bpnzac density (99.9% accuracy), while the detection of J-Uniward at 0.1 bpnzac density turned out to be hardly possible (max. 56.3% accuracy). The ensemble classifiers turned out to be an encouraging alternative to deep learning-based detection methods.

Detection of Image Steganography Using Deep Learning and Ensemble Classifiers