- First, and perhaps most important ones, we provide datasets. The main dataset is made of a set of 80,000 raw images ; the images are numbered by Digital still camera model as described in this simple text file.
Besides, for easy use by the community we also provide also several processed datasets:
Uncompressed color and grayscale image datasets (of size 512x512, 256x256 for easy use in Deep learning and various sizes).
JPEG compressed images datasets (with quality factors: 100 , 95 , 90 , 85 , 80 , 75 and various QF).
- Second we also provide the scripts that have been used to convert the raw images into jpeg format. Those python scripts use the main following library: numpy (version 1.14.5), pillow the Python Imaging Library (version 5.2), and the open-source raw image processing program Rawtherapee(version 5.7).
Those scripts are those that we have used to generate the various datasets from the raw image files.
- For any question, regarding either image datasets and/or conversion scripts, contact us at email@example.com.
- While all the materials are available under the Creative Commons BY-NC-ND license for use in any research works we kindly ask you to credit our (enormous) work by either refering to the alaska website URL or, more relevant, by simply citing one of the associated papers:
The ALASKA Steganalysis Challenge: A First Step Towards Steganalysis "into the wild", Published in the 7th ACM IH&MMSec conference.
- However, this license explicitly forbids (without prior authortisation from the authors) the used of any material for commercial purposes and the distribution of any material build upon the material provided, especially if you remix and transform the dataset.
- Official submission, counting for the final ranking, was only possible throughout the Kaggle Competitions website.
Now that the official steganalysis challenge is over, you can use this webiste to submit your solution and get it evaluted but only for fun (the official ranking is closed).
- Total cash price is $25,000 to be shared between the three best performers.
The ranking of submission is made by weighting the area under the ROC curve to focus on detection accuracy for low false probability.
Those winners will be asked to provide the source code of their detectors to get their price.
We strongly encourage the top-scoring teams to propose a paper to WIFS 2020 ; a $3000 travel grant will be offered by the IEEE for accepted papers.
- All informations about timeline, assessement of the solutions, and material are provided on the Kaggle Competitions website.
The present website is mostly intended to provide the ALASKAv2 datasets, which can be used for any research and education purposes, for longterm storing. Registered users can also submit their proposal and get it evaluated for benchmarking purpose but this is an un-official ranking ; to do so, however, you must create an account with a valid email address that will be used to diplay your username .
Those accounts will only be used for statistical purposes and communications will be made only regarding the ALASKA challenge.
Submit an answer
You must be authenticated in order to submit your steganalysis results.
As of date (dynamique) the current leaderbord is the following. Note that you can click on column headers to sort submissions according to the various scores. However the one used for the "official" ranking is only the first one, MD005 (Missed detection for False alarm of 0.05).
Missed Detection at 5% False Alarm Rate
minPE (Minimal Error Rate)
FP50 (False Positive rate at 50% Missed Detection)
- The ALASKA Steganalysis Challenge: A First Step Towards Steganalysis ``into the wild''., Published in the 7th ACM IH&MMSec conference.
- Antoine Prudhomme, for creating the present website.
- Julien Flamant, Jean-Baptiste Gobin, Bertrand De La Morlais, Florent Pergoud, Luc Rodrigues, Pascal Royer and Emile Touron for kindly provide some of their raw images as well as Dirk Borghys for the joint work on assessment of impact of raw images development on steganalysis performance (that gives birth to ALASKA dataset and challenge).
- The computer resources department of Troyes University of Technology who helped us with all their advices and suggestions.