Federated Learning for the Efficient Detection of Steganographic Threats Hidden in Image Icons

Author:

Cassavia NunziatoORCID,Caviglione LucaORCID,Guarascio MassimoORCID,Liguori AngelicaORCID,Surace Giuseppe,Zuppelli MarcoORCID

Publisher

Springer Nature Switzerland

Reference30 articles.

1. Cassavia, N., Caviglione, L., Guarascio, M., Manco, G., Zuppelli, M.: Detection of steganographic threats targeting digital images in heterogeneous ecosystems through machine learning. J. Wirel. Mob. Netw. Ubiquit. Comput. Dependable Appl. 13, 50–67 (2022)

2. Caviglione, L., Mazurczyk, W.: Never mind the malware, here’s the stegomalware. IEEE Securi. Priv. 20(5), 101–106 (2022)

3. Cheddad, A., Condell, J., Curran, K., Mc Kevitt, P.: Digital image steganography: survey and analysis of current methods. Signal Process. 90(3), 727–752 (2010)

4. Gibert, D., Mateu, C., Planes, J.: The rise of machine learning for detection and classification of malware: research developments, trends and challenges. J. Netw. Comput. Appl. 153, 102526 (2020)

5. Guarascio, M., Manco, G., Ritacco, E.: Deep learning. Encycl. Bioinform. Comput. Biol.: ABC Bioinform. 1–3, 634–647 (2018)

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