ALASKA-2 : Challenging Academic Research on Steganalysis with Realistic Images

Rémi Cogranne (1) , Quentin Giboulot (1) , Patrick Bas (2)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

This paper briefly summarizes the ALASKA#2 steganalysis challenge which has been organized on the Kaggle machine learning competition platform. We especially focus on the context, the organization (rules, timeline, evaluation and material) as well as on the outcome (number of competitors, submission, findings, and final results). While both steganography and steganalysis were new to most of the competitors, they were able to leverage their skills in Deep Learning in order to design detection methods that perform significantly better than current art in steganalysis. Despite the fact that these solutions come at an important computational cost, they clearly indicate new directions to explore in steganalysis research.

Domains: Computer Science [cs]; Image Processing [eess.IV]; Computer Science [cs]; Signal and Image Processing; Computer Science [cs]; Cryptography and Security [cs.CR]; Computer Science [cs]; Artificial Intelligence [cs.AI]; Engineering Sciences [physics]; Engineering Sciences [physics]; Signal and Image processing

Rémi Cogranne, Quentin Giboulot, Patrick Bas. ALASKA-2 : Challenging Academic Research on Steganalysis with Realistic Images. IEEE International Workshop on Information Forensics and Security, Dec 2020, New York City (Virtual Conference), United States. ⟨10.1109/WIFS49906.2020.9360896⟩. ⟨hal-02950094⟩