Breaking ALASKA

Yassine Yousfi (1) , Jan Butora (1) , Jessica Fridrich (1) , Quentin Giboulot (2)

1 Binghamton University [SUNY]
2 LM2S - Laboratoire Modélisation et Sûreté des Systèmes

This paper describes the architecture and training of detectors developed for the ALASKA steganalysis challenge. For each quality factor in the range 60-98, several multi-class tile detectors implemented as SRNets were trained on various combinations of three input channels: luminance and two chrominance channels. To accept images of arbitrary size, the detector for each quality factor was a multi-class multi-layered perceptron trained on features extracted by the tile detectors. For quality 99 and 100, a new "reverse JPEG compatibility attack" was developed and also implemented using the SRNet via the tile detector. Throughout the paper, we explain various improvements we discovered during the course of the competition and discuss the challenges we encountered and trade offs that had to be adopted in order to build a detector capable of detecting steganographic content in a stego source of great diversity.

Domains: Image Processing [eess.IV]

Yassine Yousfi, Jan Butora, Jessica Fridrich, Quentin Giboulot. Breaking ALASKA. the ACM Workshop, Jul 2019, Paris, France. pp.138-149. ⟨hal-02291306⟩